Interoperability Archives - gettectonic.com

22May

The Evolution Beyond AI Agents

The Evolution Beyond AI Agents: What Comes Next? The Rapid Progression of AI Terminology The landscape of artificial intelligence has undergone a remarkable transformation in just three years. What began with ChatGPT and generative AI as the dominant buzzwords quickly evolved into discussions about copilots, and most recently, agentic AI emerged as 2024‘s defining concept. This accelerated terminology cycle mirrors fashion industry trends more than traditional technology adoption curves. Major players including Adobe, Qualtrics, Oracle, OpenAI, and Deloitte have recently launched agentic AI platforms, joining earlier entrants like Microsoft, AWS, and Salesforce. This rapid market saturation suggests the industry may already be approaching the next conceptual shift before many organizations have fully implemented their current AI strategies. Examining the Staying Power of Agentic AI Industry analysts present diverging views on the longevity of the agentic AI concept. Brandon Purcell, a Forrester Research analyst, acknowledges the pattern of fleeting AI trends while recognizing agentic AI’s potential for greater staying power. He cites three key factors that may extend its relevance: Klaasjan Tukker, Adobe’s Senior Director of Product Marketing, draws parallels to mature technologies that have become invisible infrastructure. He predicts agentic AI will follow a similar trajectory, becoming so seamlessly integrated that users will interact with it as unconsciously as they use navigation apps or operate modern vehicles. The Automotive Sector as an AI Innovation Catalyst The automotive industry provides compelling examples of advanced AI applications that transcend current “agentic” capabilities. Modern autonomous vehicles demonstrate sophisticated AI behaviors including: These implementations suggest that what the tech industry currently labels as “agentic” may represent only an intermediate step toward more autonomous, context-aware systems. The Definitional Challenges of Agentic AI The technology sector faces significant challenges in establishing common definitions for emerging AI concepts. Adobe’s framework describes agents as systems possessing three core attributes: However, as Scott Brinker of HubSpot notes, the term “agentic” risks becoming overused and diluted as vendors apply it inconsistently across various applications and functionalities. Interoperability as the Critical Success Factor For agentic AI systems to deliver lasting value, industry observers emphasize the necessity of cross-platform compatibility. Phil Regnault of PwC highlights the reality that enterprise environments typically combine solutions from multiple vendors, creating integration challenges for AI implementations. Three critical layers require standardization: Without such standards, organizations risk creating new AI silos that mirror the limitations of legacy systems. The Future Beyond Agentic AI While agentic AI continues its maturation process, the technology sector’s relentless innovation cycle suggests the next conceptual breakthrough may emerge sooner than expected. Historical naming patterns for AI advancements indicate several possibilities: As these technologies evolve, they may shed specialized branding in favor of more utilitarian terminology, much as “software bots” became normalized after their initial hype cycle. The automotive parallel suggests that truly transformative AI implementations may become so seamlessly integrated that their underlying technology becomes invisible to end users—the ultimate measure of technological maturity. Until that point, the industry will likely continue its rapid cycle of innovation and rebranding, searching for the next paradigm that captures the imagination as powerfully as “agentic AI” has in 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 22, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Technology0 Comments

19May

U.S. Government Secures Major Discounts on Slack for Federal Agencies

U.S. Government Secures Major Discounts on Slack for Federal Agencies Through New GSA-Salesforce Partnership May 19, 2025 – In a major step to boost federal efficiency, the U.S. General Services Administration (GSA) has signed a landmark OneGov agreement with Salesforce, slashing the cost of Slack for government agencies. The deal provides deep discounts on Slack’s enterprise collaboration tools, enabling federal offices to modernize operations while cutting costs. Key Details of the Agreement Under the new terms, federal agencies will receive: These reduced rates will be available until November 30, 2025, giving agencies a six-month window to adopt the platform at significantly lower costs. Unlike past agreements, where agencies negotiated individual discounts, this deal leverages the total purchasing power of the federal government, ensuring better pricing and streamlined procurement. The move reflects the GSA’s push to centralize IT acquisitions, eliminating redundancies and maximizing savings. Leadership and Strategic Goals Josh Gruenbaum, GSA Federal Acquisition Service Commissioner, highlighted the partnership’s significance: “Through the OneGov initiative, we’re demonstrating that the federal government is a strong partner for industry, securing top-tier tools at the best value for taxpayers.” The OneGov program, backed by the current administration, aims to foster long-term partnerships with tech providers, potentially leading to more discounted offerings in the coming fiscal year. Boosting Government Productivity Slack, now part of Salesforce, serves as a unified hub for communication, app integration, and AI-driven workflows. Its adoption across federal agencies could improve interdepartmental collaboration, reduce inefficiencies, and accelerate decision-making. The inclusion of Slack AI for Enterprise introduces advanced automation, helping agencies process data faster and optimize operations—a critical advantage for large, complex organizations. A Shift in Federal Tech Procurement This agreement signals a broader move toward modernizing government IT infrastructure while controlling costs. As noted by NextGov, the deal is part of GSA’s strategy to offer cost-effective, scalable solutions under the OneGov framework. By standardizing collaboration tools across agencies, the federal government could enhance interoperability, reduce reliance on fragmented systems, and lower long-term technical debt. Looking Ahead Industry analysts suggest that this partnership could pave the way for more AI and cloud-based solutions in government. The steep discounts may drive rapid adoption, setting a precedent for future public-private tech collaborations. As agencies integrate Slack into their workflows, the impact on federal productivity and service delivery will be closely monitored—potentially serving as a model for future digital transformation efforts. Sources: BizSugar, Investing.com, NextGov Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 19, 2025in Data, Enterprise, Public Sector Solutions, Salesforce, Salesforce Public Sector Solutions, Salesforce Slack0 Comments

02May

The AI Agent Revolution

The AI Agent Revolution: How Tectonic is Unifying Disparate AI Systems for Enterprises AI agents are proliferating at breakneck speed—embedded in platforms, deployed as standalone apps, and built on proprietary or open-source SDKs. Yet as these intelligent systems multiply, enterprises face a critical challenge: getting them to communicate, collaborate, and scale effectively across complex IT environments. Recent moves by Tectonic, Salesforce, and Google Cloud highlight the next frontier of enterprise AI: seamless, cross-platform agent orchestration. We’ve reached an inflection point where human-AI synergy can transform business operations—but only if organizations can unify their agent ecosystems. The AI Agent Collaboration Challenge Today’s enterprises use AI agents for:✔ Salesforce’s Agentforce (CRM automation)✔ Google’s Agentspace (cloud-based workflows)✔ Custom agents (built on Vertex AI, OpenAI, or open-source models) But without interoperability, these agents operate in silos—limiting their potential. Tectonic bridges this gap with secure, enterprise-grade agent orchestration, enabling businesses to: Tectonic and Supported Agent OS: The Glue Holding AI Ecosystems Together Tectonic and Agent Operating Systems (OS) are business-focused platform for orchestrating AI agents across enterprise environments. An “agent operating system” (AOS) is a type of operating system designed to facilitate the development, deployment, and management of AI agents, which are software systems that can act autonomously to achieve goals. AOS systems aim to provide a platform for AI agents to operate efficiently and effectively, offering features like resource management, context switching, and tool integration. AIOS, for example, is a particular implementation of this concept that aims to address the challenges of managing large language model (LLM)-based AI agents How It Works Real-World Use Cases 1. Salesforce + Google Gemini: Smarter CRM Salesforce’s Agentforce now integrates Google Gemini, enabling:🔹 Better RAG (Retrieval-Augmented Generation) for faster, more accurate customer responses🔹 Predictive trend analysis embedded directly in CRM workflows Tectonic’s Role: Deploys multi-agent solutions that turn AI insights into actionable items—like auto-recommending next steps for sales teams. 2. Retail: Unified Customer Experiences A retailer combines: Result: Customers get instant, accurate updates on orders—no manual backend checks required. 3. Financial Services: AI-Powered Risk Analysis Banks use: Outcome: Suspicious transactions trigger automated compliance workflows without leaving Salesforce. Tectonic’s AI Activation Path: From Pilot to Production For enterprises ready to scale AI agents, Tectonic offers a rapid deployment framework:✅ Discovery and Road Mapping – Co-design high-impact use cases✅ Rapid Implementation – Deploy working agents in sandbox environments✅ Pre-Built Industry Libraries – Accelerate time-to-value The Future: Harmonized AI Ecosystems The biggest barrier to AI adoption isn’t technology—it’s fragmentation. With the Agent OS in place, businesses can finally:✔ Break down silos between Salesforce, Google Cloud, and custom AI✔ Automate complex workflows end-to-end✔ Scale AI responsibly with enterprise-grade governance The bottom line? AI agents are powerful alone—but unstoppable when unified. Ready to orchestrate your AI ecosystem?Discover how Tectonic’s Agentforce approach can transform your enterprise AI strategy. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

May 2, 2025in Data, Enterprise, Google, Salesforce, Technology0 Comments

Google and Salesforce Expand Partnership

19Apr

Google Unveils Agent2Agent (A2A)

Google Unveils Agent2Agent (A2A): An Open Protocol for AI Agents to Collaborate Directly Google has introduced the Agent2Agent Protocol (A2A), a new open standard that enables AI agents to communicate and collaborate seamlessly—regardless of their underlying framework, developer, or deployment environment. If the Model Context Protocol (MCP) gave agents a structured way to interact with tools, A2A takes it a step further by allowing them to work together as a team. This marks a significant step toward standardizing how autonomous AI systems operate in real-world scenarios. Key Highlights: How A2A Works Think of A2A as a universal language for AI agents—it defines how they: Crucially, A2A is designed for enterprise use from the ground up, with built-in support for:✔ Authentication & security✔ Push notifications & streaming updates✔ Human-in-the-loop workflows Why This Matters A2A could do for AI agents what HTTP did for the web—eliminating vendor lock-in and enabling businesses to mix-and-match agents across HR, CRM, and supply chain systems without custom integrations. Google likens the relationship between A2A and MCP to mechanics working on a car: Designed for Enterprise Security & Flexibility A2A supports opaque agents (those that don’t expose internal logic), making it ideal for secure, modular enterprise deployments. Instead of syncing internal states, agents share context via structured “Tasks”, which include: Communication happens via standard formats like HTTP, JSON-RPC, and SSE for real-time streaming. Available Now—With More to Come The initial open-source spec is live on GitHub, with SDKs, sample agents, and integrations for frameworks like: Google is inviting community contributions ahead of a production-ready 1.0 release later this year. The Bigger Picture If A2A gains widespread adoption—as its strong early backing suggests—it could accelerate the AI agent ecosystem much like Kubernetes did for cloud apps or OAuth for secure access. By solving interoperability at the protocol level, A2A paves the way for businesses to deploy a cohesive digital workforce composed of diverse, specialized agents. For enterprises future-proofing their AI strategy, A2A is a development worth watching closely. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 19, 2025in Agentforce Platform, Data, Enterprise, Google, Salesforce0 Comments

15Apr

Salesforce Unveils Blueprint for the Agentic AI Era

A Roadmap for AI Maturity: From Chatbots to Autonomous Agents Salesforce has introduced a new Agentic Maturity Model, providing businesses with a structured framework to evolve from basic AI chatbots to fully autonomous, collaborative AI agents. With 84% of CIOs believing AI will be as transformative as the internet—yet struggling with deployment—this model offers a clear pathway to scale AI effectively. The Four Stages of Agentic AI Maturity Salesforce’s model defines four progressive stages of AI agent sophistication: 1️⃣ Chatbots & Co-Pilots (Stage 0 → 1) 2️⃣ Information Retrieval Agents (Stage 1 → 2) 3️⃣ Simple Orchestration (Single Domain) → Complex Orchestration (Multiple Domains) (Stage 2 → 3) 4️⃣ Multi-Agent Orchestration (Stage 3 → 4) Why This Model Matters Many businesses deploy AI quickly but struggle to scale due to:🔹 Unclear governance🔹 Data silos🔹 Security concerns🔹 Lack of human-AI collaboration strategies Shibani Ahuja, SVP of Enterprise IT Strategy at Salesforce, emphasizes: “Scaling AI effectively requires a phased approach. This framework helps organizations progress toward higher maturity—balancing innovation with security and operational readiness.” Key Recommendations for Advancement ✅ Start with high-impact use cases where chatbots fall short.✅ Build governance early—define testing, security, and accountability.✅ Prepare data ecosystems for AI interoperability.✅ Foster human-AI collaboration—agents should augment, not replace, teams. The Future: AI That Works Like a Well-Oiled Team The ultimate vision? AI agents that: Salesforce’s model provides the playbook to get there—helping businesses move from experimentation to enterprise-wide AI transformation. Next Step: Assess where your organization stands—and start climbing the maturity ladder. Contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

April 15, 2025in Agentforce Platform, Agentic AI, AI Chatbots, Data, Enterprise, Salesforce0 Comments

19Mar

How Salesforce’s 5-Level Framework for AI Agents Cuts Through the Hype

The tech industry is abuzz with talk of AI agents, but what can they actually accomplish? Amid the noise, Salesforce has introduced a practical five-level framework—the Agentic Maturity Model—that clarifies the real capabilities and limitations of today’s AI agents. The Problem with AI Agent Hype AI agents are often overpromised, vaguely defined, and limited by ecosystem barriers. Major players like Microsoft and Google tout AI agents for everything from enterprise workflows to personal computing, yet many of these tools remain constrained by data silos and interoperability issues. Salesforce’s framework provides a structured way to assess AI agent maturity, helping businesses distinguish between basic automation and truly intelligent, cross-platform AI systems. The 5 Levels of AI Agent Maturity Level 0: Fixed Rules & Repetitive Tasks Level 1: Information Retrieval Agents Level 2: Simple Orchestration, Single Domain Level 3: Complex Orchestration, Multiple Domains Level 4: Multi-Agent Orchestration Why This Framework Matters Salesforce’s model demystifies AI agent capabilities, helping businesses:✅ Evaluate vendor claims (Is it Level 2 or Level 4?).✅ Plan AI adoption (Start with Level 0 automation, then scale up).✅ Avoid ecosystem lock-in by understanding data interoperability challenges. Final Verdict: A Much-Needed Reality Check While AI agents hold immense potential, most current implementations are far from autonomous. Salesforce’s framework provides a clear, honest roadmap—helping businesses cut through the hype and adopt AI agents strategically. For now, Levels 0-2 are widely achievable, while Levels 3-4 remain aspirational for most organizations. The key takeaway? AI agents are evolving, but true cross-platform intelligence is still a work in progress. What’s Next?Businesses should: Salesforce’s framework is a wake-up call: AI agents are powerful, but not magic. The future lies in practical, phased adoption—not blind hype. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 19, 2025in Agentforce Platform, Data, Enterprise, Google, Salesforce

05Mar

AI Agents Are the Future of Enterprise

AI Agents Are the Future of Enterprise—But They Need the Right Architecture AI agents are poised to revolutionize enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. However, the biggest challenge isn’t improving models—it’s building the infrastructure to support them. Agents require seamless access to data, tools, and the ability to share insights across systems—with outputs usable by multiple services, including other agents. This isn’t just an AI challenge; it’s an infrastructure and data interoperability problem. Traditional approaches—like chaining commands—won’t cut it. Instead, enterprises need an event-driven architecture (EDA) powered by real-time data streams. As HubSpot CTO Dharmesh Shah put it, “Agents are the new apps.” To unlock their potential, businesses must invest in the right design patterns from the start. This insight explores why EDA is critical for scaling AI agents and integrating them into modern enterprise systems. The Evolution of AI: From Predictive Models to Autonomous Agents AI has progressed through three key waves, each overcoming—but also introducing—new limitations. 1. The First Wave: Predictive Models Early AI relied on traditional machine learning (ML) for narrow, domain-specific tasks. These models were rigid, requiring extensive retraining for new use cases. Limitations: 2. The Second Wave: Generative AI Generative AI, powered by large language models (LLMs), introduced general-purpose intelligence. Unlike predictive models, LLMs could handle diverse tasks—from text generation to code synthesis. Limitations: For example, asking an LLM to recommend an insurance policy based on a user’s health history fails—unless the model can dynamically retrieve personal data. 3. The Third Wave: Compound AI & Agentic Systems To overcome these gaps, Compound AI systems combine LLMs with: But even RAG has limits—it relies on fixed workflows, making it inflexible for dynamic tasks. Enter AI agents: autonomous systems that reason, plan, and adapt in real time. Why Agents Are the Next Frontier Salesforce CEO Marc Benioff recently noted that LLMs are hitting their limits, and the future lies in autonomous agents. Unlike static models, agents: Key Agent Design Patterns These patterns enable Agentic RAG, where retrieval isn’t fixed but adaptive—agents decide what data to fetch based on context. The Scaling Challenge: It’s an Infrastructure Problem Agents need real-time data access and seamless interoperability—but connecting them via APIs creates tight coupling, leading to: The Solution: Event-Driven Architecture (EDA) EDA decouples agents using asynchronous event streams (e.g., Kafka, Redpanda). Benefits:✅ Loose coupling – Agents communicate without direct dependencies.✅ Real-time reactivity – Instant responses to changing data.✅ Scalability – New agents join without redesigning the system.✅ Resilience – Failures don’t cascade. Example: An agent analyzing customer data publishes an event—other agents, CRMs, or analytics tools consume it without explicit coordination. Why EDA is the Future for AI Agents Just as microservices replaced monoliths, EDA will replace rigid AI pipelines. Early adopters (like Facebook with scalable infrastructure) outcompeted those that couldn’t scale (like Friendster). The same will happen with AI agents. Enterprises that embrace event-driven agents will: The Bottom Line AI agents are the next evolution of enterprise software—but without EDA, they’ll hit a wall. Companies that invest in event-driven infrastructure today will lead the next wave of AI innovation. The rest? They’ll struggle to keep up. Ready to future-proof your AI strategy? AI Agents Are the Future of Enterprise. The time to build for agents is now. Contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 5, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce

04Mar

The Future of Healthcare is Digital

Transforming Healthcare with Salesforce Health Cloud In our rapidly evolving healthcare world, digital transformation is no longer optional—it’s essential. Healthcare organizations must leverage innovative solutions to improve patient outcomes, streamline operations, and drive financial growth. Salesforce Health Cloud empowers providers, payers, and life sciences companies to deliver personalized, connected, and data-driven care. The Urgency of Digital Transformation in Healthcare Healthcare organizations face increasing pressure from regulatory requirements, patient expectations for seamless digital experiences, and operational inefficiencies. Traditional systems are often fragmented, making it difficult to provide a unified patient experience. Salesforce Health Cloud addresses these challenges by enhancing communication, centralizing data, and optimizing workflows. Key Benefits of Salesforce Health Cloud ✅ Enhanced Patient EngagementPatients expect convenience, transparency, and personalization. Health Cloud delivers seamless communication through patient portals, automated reminders, and AI-driven insights—leading to higher engagement and improved adherence to treatment plans. ✅ 360-Degree Patient ViewBy integrating data from electronic health records (EHRs), wearable devices, and other sources, Health Cloud provides a unified patient profile. This empowers providers and payers to make data-driven decisions that improve both clinical and business outcomes. ✅ Operational Efficiency & Cost ReductionHealth Cloud automates workflows, reduces redundancies, and improves productivity by integrating clinical, operational, and administrative processes. Real-time collaboration enhances care coordination across teams and organizations. ✅ AI-Powered Insights for Better Decision-MakingWith AI-driven analytics and predictive insights, healthcare organizations can identify at-risk populations, optimize resource allocation, and enhance population health management. Salesforce Einstein AI helps forecast trends and personalize care recommendations. ✅ Interoperability & Regulatory ComplianceNavigating complex healthcare regulations is easier with Health Cloud’s secure architecture, ensuring compliance while enabling seamless data exchange between systems and stakeholders. Why Healthcare Organizations Should Adopt Health Cloud Investing in digital transformation provides a competitive advantage by improving patient satisfaction, reducing operational costs, and driving better health outcomes. Salesforce Health Cloud enables organizations to future-proof their operations, boost revenue, and implement a seamless, value-based care model. Use Cases of Salesforce Health Cloud 🏥 Health Insurers – Improve member engagement, claims processing, and care management to enhance efficiency and reduce costs. 🏥 Hospitals & Health Systems – Streamline care coordination, minimize readmissions, and enhance patient experiences with personalized care plans. 🏥 Home Healthcare & Telemedicine – Enable remote patient monitoring and virtual care, improving accessibility while reducing hospital visits. 🏥 Life Sciences Companies – Accelerate drug development, streamline clinical trials, and enhance collaboration with providers and patients. The Future of Healthcare is Digital Salesforce Health Cloud is at the forefront of healthcare’s digital revolution. By leveraging AI, automation, and seamless integrations, healthcare leaders can improve patient experiences, drive operational efficiency, and ensure long-term success. Now is the time to embrace a data-driven approach to healthcare management. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

March 4, 2025in Data, Salesforce, Salesforce Einstein, Salesforce Health Cloud

24Feb

The Event-Driven Paradigm for Next-Generation AI Agents

The Infrastructure Imperative for AI Evolution The enterprise landscape stands at an inflection point where AI agents promise autonomous decision-making and adaptive workflows at scale. However, the critical barrier to realizing this potential isn’t model sophistication—it’s architectural. True agentic systems require: These requirements fundamentally represent an infrastructure challenge that demands event-driven architecture (EDA) as the foundational framework for agent deployment and scaling. The Three Waves of AI Evolution First Wave: Predictive Models Characterized by: These deterministic systems excelled at specialized tasks but proved rigid and unscalable across business functions. Second Wave: Generative Models Marked by breakthroughs in: However, these models remained constrained by: Third Wave: Agentic Systems Emerging capabilities include: This evolution shifts focus from model architecture to system architecture, where EDA becomes the critical enabler. The Compound AI Advantage Modern agent systems combine multiple architectural components: This compound approach overcomes the limitations of standalone models through: Event-Driven Architecture: The Nervous System for Agents Core EDA Principles for AI Systems Implementation Benefits Architectural Patterns for Agentic Systems 1. Reflective Processing <img src=”reflection-pattern.png” width=”400″ alt=”Reflection design pattern diagram”> Agents employ meta-cognition to: 2. Dynamic Tool Orchestration <img src=”tool-use-pattern.png” width=”400″ alt=”Tool use design pattern diagram”> Capabilities include: 3. Hierarchical Planning <img src=”planning-pattern.png” width=”400″ alt=”Planning design pattern diagram”> Features: 4. Collaborative Multi-Agent Systems <img src=”multi-agent-pattern.png” width=”400″ alt=”Multi-agent collaboration diagram”> Enables: The Enterprise Integration Challenge Critical Success Factors Implementation Roadmap Phase 1: Foundation Phase 2: Capability Expansion Phase 3: Optimization The Competitive Imperative Enterprise readiness data reveals: Early adopters of event-driven agent architectures gain: The transition to agentic operations represents not just technological evolution but fundamental business transformation. Organizations that implement EDA foundations today will dominate the AI-powered enterprise landscape of tomorrow. Those failing to adapt risk joining the legacy systems they currently maintain—as historical footnotes in the annals of digital transformation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 24, 2025in Data, Enterprise, Technology

24Feb

Physicians Embrace AI for Documentation

Physicians Embrace AI for Documentation, But Diagnostic Concerns Persist A new survey reveals growing physician adoption of AI for clinical documentation and administrative tasks, yet lingering skepticism remains about its role in medical decision-making. According to Athenahealth’s fourth annual Physician Sentiment Survey, conducted by The Harris Poll, 68% of physicians using AI in their practice report increased reliance on the technology for clinical documentation over the past year. The top AI applications they value include: Shifting Perceptions of AI in Healthcare Physician attitudes toward AI are evolving: However, significant concerns persist: Interoperability Remains a Key Challenge While 91% of physicians agree that better data exchange between systems would improve patient outcomes, more than half report weekly frustrations accessing patient records from other providers. Additionally: Patient Portals: A Double-Edged Sword Though widely used, patient portals contribute to physician burnout: Job Satisfaction Shows Modest Gains Despite ongoing challenges, physician morale appears to be improving: The Path Forward: Balancing Innovation and Trust Dr. Nele Jessel, Chief Medical Officer at Athenahealth, noted: “AI is moving from hype to reality, and physicians are seeing real benefits in reducing administrative burdens. But we must ensure these tools are designed to support—not replace—clinical judgment, while improving efficiency across practices of all sizes.” As AI adoption grows, addressing diagnostic reliability, interoperability gaps, and workload pressures will be critical to sustaining physician trust and improving patient care. Key Takeaways:✅ AI use is rising for documentation, but diagnostic trust lags.⚠️ Interoperability frustrations persist, driving physician stress.📈 Job satisfaction is improving, yet burnout risks remain.🔮 Future success hinges on human-AI collaboration and better system integration. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 24, 2025in AI Tools, Data, Life Sciences Cloud, Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Technology

12Feb

5 Key Use Cases for Generative AI in Healthcare Documentation

Generative AI (GenAI) holds significant promise for improving healthcare documentation, but clear regulations and standards are needed to maximize benefits while minimizing risks. Healthcare documentation encompasses medical histories, clinical notes, diagnostic results, treatment plans, prescriptions, and billing records. Studies show that clinicians spend more time on documentation than patient care—a major contributor to burnout. GenAI can help by automating electronic health record (EHR) data entry and drafting medical notes for clinician review. According to a February 2025 American Medical Association (AMA) survey, early GenAI adoption in healthcare focuses on administrative tasks that enhance documentation quality and efficiency. For example, Microsoft’s Dax Copilot saves clinicians five minutes per patient encounter, while Oracle Health Clinical AI Agent reduces documentation time by nearly 30%. Here are five key ways GenAI improves healthcare documentation: 1. Streamline Workflows GenAI reduces administrative burdens by automating documentation tasks, allowing clinicians to focus more on patient care. Key applications include: A JAMA Network Open (2024) study found AI-generated draft replies to patient messages reduced provider workload and emotional exhaustion, suggesting strong potential for workflow efficiency. 2. Improve Data Accuracy GenAI enhances documentation precision by identifying missing or inconsistent data. Applications include: By minimizing manual entry, GenAI helps prevent errors that lead to billing delays or compliance issues. 3. Optimize Medical Data Approximately 80% of healthcare data is unstructured (e.g., physician notes, scanned documents). GenAI transforms this into structured, usable formats by: This optimization improves interoperability and speeds up decision-making. 4. Reduce Clinician Burnout Physician burnout is often linked to excessive documentation. GenAI alleviates stress by: A UC San Diego (2024) study found that AI-assisted documentation helps clinicians engage more with patients, improving satisfaction and outcomes. 5. Enhance Patient Engagement GenAI improves patient interactions by: By reducing screen time during visits, GenAI helps clinicians build stronger patient relationships. Best Practices for GenAI in Healthcare Documentation To ensure safe and effective AI adoption:✔ Start with pilot programs – Test AI tools in controlled settings.✔ Train clinicians on AI review – Ensure staff can validate AI-generated content.✔ Notify patients about AI use – Maintain transparency in documentation.✔ Secure patient data – Encrypt and de-identify protected health information (PHI).✔ Maintain audit logs – Track AI-generated documentation for accuracy and compliance. Challenges & Future Outlook GenAI faces hurdles in data privacy, regulatory compliance, and liability. Until formal standards emerge, frameworks like the WHO’s AI Ethics Guidelines and Coalition for Health AI (CHAI) Assurance Standards can help guide responsible use. As multimodal AI models advance, GenAI will better adapt to clinician workflows. However, strong governance is essential to balance innovation with patient safety. Conclusion GenAI is transforming healthcare documentation by reducing burnout, improving accuracy, and enhancing patient engagement. By implementing best practices and robust governance, healthcare organizations can harness AI’s potential while mitigating risks. Content updated April 2025. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

February 12, 2025in AI Tools, Data, Generative AI, Salesforce, Salesforce Health and Life Sciences Solutions

23Dec

AI Transforming Precision Medicine

How AI-Driven Data Curation is Transforming Precision Medicine Precision medicine—a healthcare approach that personalizes disease prevention and treatment based on insights into a patient’s genes, environment, and behavior—holds incredible promise. However, its success depends on high-quality, curated data from sources like electronic health records (EHRs). This reliance creates significant challenges for healthcare providers and researchers. Can artificial intelligence (AI) help address these hurdles? AI-enabled data curation is already making strides in advancing precision medicine, particularly in oncology. By analyzing vast datasets, including structured and unstructured information, AI is helping healthcare organizations accelerate research and improve patient outcomes. Data Curation Challenges in Precision Medicine Real-world data (RWD) is a key driver of precision medicine, but processing this data is fraught with challenges. According to Dr. C.K. Wang, Chief Medical Officer at COTA, Inc., EHRs provide unprecedented access to detailed patient information, enabling deeper insights into care patterns. However, much of this data resides in unstructured formats, such as clinicians’ notes, making it difficult to extract and analyze. “To transform this unstructured data into actionable insights, significant human expertise and resources are required,” Wang explained. While AI tools like COTA’s CAILIN, which uses advanced search capabilities, streamline this process, human involvement remains essential. Wang emphasized that even with the rapid advancements in AI, healthcare data curation requires expert oversight to ensure quality and reliability. “The adage ‘junk in, junk out’ applies here—without high-quality training data, AI cannot generate meaningful insights,” he noted. PHI and COTA: A Collaborative Approach to AI-Driven Curation To overcome these challenges, Precision Health Informatics (PHI), a subsidiary of Texas Oncology, partnered with COTA to enhance their data curation capabilities. The collaboration aims to integrate structured and unstructured data, including clinician notes and patient-reported outcomes, into a unified resource for precision medicine. PHI’s database, which represents 1.6 million patient journeys, provides a rich resource for hypothesis-driven studies and clinical trial enrichment. However, much of this data was siloed or unstructured, requiring advanced tools and expert intervention. Lori Brisbin, Chief Operating Officer at PHI, highlighted the importance of partnering with a data analytics leader. “COTA’s strong clinical knowledge in oncology allowed them to identify data gaps and recommend improvements,” she said. This partnership is yielding significant results, including a high data attrition rate of 87%—far surpassing the industry average of 50% for similar projects. The Role of AI in Cancer Care AI tools like CAILIN are helping PHI and COTA refine data curation processes by: Brisbin likened the role of AI to sorting images: “If you’re looking for German shepherds, AI will narrow the search but might include similar images, like wolves or huskies. Experts are still needed to validate and refine the results.” Building the Foundation for Better Outcomes The integration of high-quality RWD into analytics efforts is reshaping precision medicine. While clinical trial data offers valuable insights, it often lacks the variability seen in real-world scenarios. Adding RWD to these datasets helps expand the scope of research and ensure broader applicability. For instance, cancer care guidelines developed with RWD can account for diverse patient populations and treatment approaches. COTA’s work with PHI underscores the value of collaborative data curation, with AI streamlining processes and human experts ensuring accuracy. The Future of AI in Precision Medicine As healthcare organizations invest in data-driven innovation, AI will play an increasingly pivotal role in enabling precision medicine. However, challenges remain. Wang noted that gaps in EHR data, such as missing survival metrics, can undermine oncological outcomes research. Advances in interoperability and external data sources will be key to addressing these issues. “The foundation of our partnership is built on leveraging data insights to enhance care quality and improve operational efficiency,” Wang said. Through AI-powered tools and meaningful partnerships, precision medicine is poised to deliver transformative results, empowering providers to offer tailored treatments that improve patient outcomes at scale. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

December 23, 2024in AI Tools, Artificial Intelligence, Data

22Nov

Value-Based Care Technologies

Essential Technologies for Value-Based Care Success As healthcare providers increasingly adopt value-based care, they must invest in the right technologies and resources to succeed in this model, which incentivizes high-quality, cost-effective care. Value-Based Care Technologies tie reimbursement to care quality, making providers accountable for patient outcomes while providing resources to enhance care. As of 2021, nearly 60% of healthcare payments were already tied to value-based models, according to the Health Care Payment Learning and Action Network (HCP LAN). While partnerships can initiate value-based care, providers must invest in the right technology to fully achieve the intended outcomes. Health Information Exchange (HIE) A robust health information exchange (HIE) is fundamental to value-based care, as it enables providers and payers to access high-quality data seamlessly. HIE allows healthcare professionals to share patients’ medical information electronically across organizations, promoting care coordination by giving providers a comprehensive view of patient needs. For patients, HIE enables more informed involvement in their care by making their health data accessible across specialists, labs, and pharmacies. While joining an HIE may involve new technology investments and workflow adjustments, it ultimately enhances provider access to critical health data. Population Health Management Tools Population health management tools help providers assess health outcomes within groups rather than focusing on individuals alone. These tools aggregate and analyze data, allowing practices to identify high-risk patients and create targeted interventions. This not only enhances health outcomes but can also reduce costs by avoiding expensive treatments. Patient engagement tools, such as telehealth and remote patient monitoring, are essential in population health management, especially for monitoring high-risk patients when in-person care is not feasible. Digital surveys integrated within patient portals can provide insights into social determinants of health, adding a broader context to patient needs. Data Analytics Data analytics transform healthcare data into actionable insights across four types: descriptive, diagnostic, predictive, and prescriptive. Providers can use these analytics to reduce hospital readmissions, predict diseases, and identify chronic illnesses. Data integration and risk stratification capabilities are especially valuable in value-based care, enabling providers to track patient health outcomes effectively and prioritize high-risk cases. Artificial Intelligence & Machine Learning AI and machine learning support many data analytics functions, helping identify patient needs and easing administrative burdens. Given staffing shortages and burnout—reported by 63% of physicians in 2021, according to the American Medical Association (AMA)—AI can automate tasks like documentation, charting, and scheduling, allowing providers to focus more on patient care. Additionally, AI-driven automation in revenue cycle management tasks, such as billing and coding, can reduce the administrative workload associated with value-based care. Price Transparency Technology Price transparency empowers patients to seek cost-effective care, a core principle of value-based models. When providers comply with transparency regulations, patients can better understand their costs and make informed decisions. For providers, leveraging price transparency tools ensures compliance and facilitates partnerships with payers by enabling more effective negotiation, which supports the overall goals of value-based care. As healthcare continues shifting to value-based models, investing in these technologies is critical for providers aiming for long-term success. While these tools rdo equire substantial investment, they are essential for improving patient outcomes, optimizing care quality, and ensuring sustainability in value-based care. When evaluating and choosing healthcare technology tools, contact Tectonic for help. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

November 22, 2024in AI Tools, Artificial Intelligence, Data, Life Sciences Cloud, Salesforce, Salesforce Development Services, Salesforce Foundations, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Optimization, Technology

17Nov

NetSuite Salesforce Collaboration

NetSuite Bets on Strategic Growth and Embraces Collaboration with Salesforce Growing on All Fronts At SuiteWorld 2024, the theme, “All Systems Grow,” reflected a pivotal moment for NetSuite. While the event lacked groundbreaking announcements, it showcased a fulfillment of past promises and a notable strategic shift toward openness and collaboration. Oracle and NetSuite are now welcoming competitors as partners, signaling a move toward interoperability that could redefine their market positioning. With over 40,000 customers, NetSuite continues its strong growth in the ERP space, particularly among SMBs. The company’s Q3 sales surged 20% year-over-year, underlining its momentum in the mid-market. Beyond traditional ERP capabilities, NetSuite’s expanded suite of solutions positions it as more than just an ERP provider. Delivering on AI Innovations While there were no splashy acquisitions, NetSuite made significant strides by rolling out 170 new modules and features, many leveraging AI. These enhancements blend predictive AI and generative AI to increase accuracy and user productivity. These updates aim to elevate both the platform’s quality and the efficiency of its users. Redwood Design: A Transformative User Experience NetSuite is adopting Oracle’s Redwood design language, promising a more intuitive and user-friendly interface. While Redwood is not new, its phased rollout within NetSuite is a significant step forward. Notable Additions: SuiteProcurement and Salesforce Integration SuiteProcurement: NetSuite’s new procurement automation solution integrates directly with Amazon Business and Staples Business Advantage, automating ordering, invoicing, approvals, and deliveries. Plans are underway to expand vendor support, offering broader applicability in the future. Salesforce Partnership: NetSuite’s most significant announcement was its strategic partnership with Salesforce, enabling real-time data exchange between the platforms. Evan Goldberg, NetSuite’s founder and EVP, explained the rationale:“It’s up to the customer to decide what software they want to use.” The partnership reflects NetSuite’s commitment to addressing customer needs, with more SaaS integrations expected in the future. Expanding Field Service Management (FSM) NetSuite’s Field Service Management (FSM) capabilities, acquired last year, are now better integrated into its platform. While development progress has been slower than anticipated, significant enhancements are expected in the coming year, leveraging Oracle technology to extend FSM’s functionality across industries. And Field Service Management is available in Salesforce, as well. Positioned for Continued SMB Growth NetSuite’s investments are yielding results, as demonstrated by its rapid growth and deeper integration of Oracle technology. The NetSuite Analytics Data Warehouse and Enterprise Performance Management are driving adoption among existing users, showcasing the platform’s scalability. NetSuite’s ability to quickly integrate Oracle updates into its infrastructure gives it a competitive edge, ensuring customers benefit from the latest innovations without delays. With its robust feature set, AI-powered tools, and strategic partnerships like the one with Salesforce, NetSuite has strengthened its position as a go-to ERP platform for SMBs. Its consistent 20% year-over-year growth indicates a bright future, making it an increasingly attractive option for mid-market businesses. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

November 17, 2024in Data, Enterprise, Generative AI, Salesforce, Technology

24Oct

Rise of Agentforce

The Rise of Agentforce: How AI Agents Are Shaping the Future of Work Salesforce wrapped up its annual Dreamforce conference this September, leaving attendees with more than just memories of John Mulaney’s quips. As the swarms of Waymos ferried participants across a cleaner-than-usual San Francisco, it became clear that AI-powered agents—dubbed Agentforce—are poised to transform the workplace. These agents, controlled within Salesforce’s ecosystem, could significantly change how work is done and how customer experiences are delivered. Dreamforce has always been known for its bold predictions about the future, but this year’s vision of AI-based agents felt particularly compelling. These agents represent the next frontier in workplace automation, but as exciting as this future is, some important questions remain. Reality Check on the Agentforce Vision During his keynote, Salesforce CEO Marc Benioff raised an interesting point: “Why would our agents be so low-hallucinogenic?” While the agents have access to vast amounts of data, workflows, and services, they currently function best within Salesforce’s own environment. Benioff even made the claim that Salesforce pioneered prompt engineering—a statement that, for some, might have evoked a scene from Austin Powers, with Dr. Evil humorously taking credit for inventing the question mark. But can Salesforce fully realize its vision for Agentforce? If they succeed, it could be transformative for how work gets done. However, as with many AI-driven innovations, the real question lies in interoperability. The Open vs. Closed Debate As powerful as Salesforce’s ecosystem is, not all business data and workflows live within it. If the future of work involves a network of AI agents working together, how far can a closed ecosystem like Salesforce’s really go? Apple, Microsoft, Amazon, and other tech giants also have their sights set on AI-driven agents, and the race is on to own this massive opportunity. As we’ve seen in previous waves of technology, this raises familiar debates about open versus closed systems. Without a standard for agents to work together across platforms, businesses could find themselves limited. Closed ecosystems may help solve some problems, but to unlock the full potential of AI agents, they must be able to operate seamlessly across different platforms and boundaries. Looking to the Open Web for Inspiration The solution may lie in the same principles that guide the open web. Just as mobile apps often require a web view to enable an array of outcomes, the same might be necessary in the multi-agent landscape. Tools like Slack’s Block Kit framework allow for simple agent interactions, but they aren’t enough for more complex use cases. Take Clockwise Prism, for example—a sophisticated scheduling agent designed to find meeting times when there’s no obvious availability. When integrated with other agents to secure that critical meeting, businesses will need a flexible interface to explore multiple scheduling options. A web view for agents could be the key. The Need for an Open Multi-Agent Standard Benioff repeatedly stressed that businesses don’t want “DIY agents.” Enterprises seek controlled, repeatable workflows that deliver consistent value—but they also don’t want to be siloed. This is why the future requires an open standard for agents to collaborate across ecosystems and platforms. Imagine initiating a set of work agents from within an Atlassian Jira ticket that’s connected to a Salesforce customer case—or vice versa. For agents to seamlessly interact regardless of the system they originate from, a standard is needed. This would allow businesses to deploy agents in a way that’s consistent, integrated, and scalable. User Experience and Human-in-the-Loop: Crucial Elements for AI Agents A significant insight from the integration of LangChain with Assistant-UI highlighted a crucial factor: user experience (UX). Whether it’s streaming, generative interfaces, or human-in-the-loop functionality, the UX of AI agents is critical. While agents need to respond quickly and efficiently, businesses must have the ability to involve humans in decision-making when necessary. This principle of human-in-the-loop is key to the agent’s scheduling process. While automation is the goal, involving the user at crucial points—such as confirming scheduling options—ensures that the agent remains reliable and adaptable. Any future standard must prioritize this capability, allowing for user involvement where necessary, while also enabling full automation when confidence levels are high. Generative or Native UI? The discussion about user interfaces for agents often leads to a debate between generative UI and native UI. The latter may be the better approach. A native UI, controlled by the responding service or agent, ensures the interface is tailored to the context and specifics of the agent’s task. Whether this UI is rendered using AI or not is an implementation detail that can vary depending on the service. What matters is that the UI feels native to the agent’s task, making the user experience seamless and intuitive. What’s Next? The Push for an Open Multi-Agent Future As we look ahead to the multi-agent future, the need for an open standard is more pressing than ever. At Clockwise, we’ve drafted something we’re calling the Open Multi-Agent Protocol (OMAP), which we hope will foster collaboration and innovation in this space. The future of work is rapidly approaching, where new roles—like Agent Orchestrators—will emerge, enabling people to leverage AI agents in unprecedented ways. While Salesforce’s vision for Agentforce is ambitious, the key to unlocking its full potential lies in creating a standard that allows agents to work together, across platforms, and beyond the boundaries of closed ecosystems. With the right approach, we can create a future where AI agents transform work in ways we’re only beginning to imagine. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data

October 24, 2024in Data, Enterprise, Salesforce, Technology

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