Assessing Agentic AI Project Viability

Let's be real, the secret to Agentic AI working well in businesses is building trust, making sure things are super reliable, and using good systems engineering; it's all about a strong base for these smart agents. Here’s the uncomfortable math: agents fail exponentially. A 10-step workflow at 95% per-step accuracy delivers ~60% end-to-end reliability. That’s not “pretty good.” That’s unshippable for anything that touches money, customers, or compliance. And the worst failures are invisible: - Infinite loops that burn tokens like a financial denial-of-service attack - Silent failures where the API call “succeeds” but the business outcome is wrong - Hallucinated parameters that pass monitoring while breaking reality - Write actions that turn a tiny mistake into a big blast radius The fix is not “better prompting.” It’s an Architecture of Trust: treat agents like unreliable components and wrap them in deterministic framework. Minimum Viable Trust Stack (MVTS): - Strict schemas for every tool input/output - Regression suite (golden datasets) on every commit - Circuit breakers for steps, time, and cost - Incident replay to reproduce failures deterministically - OpenTelemetry traces so you can debug behavior, not vibes Then mature your operating model: - Evals that move from vibes to metrics, judges, simulations, and canaries - Observability that captures decision records and full execution traces - FinOps at span-level so runaway reasoning doesn’t become your cloud bill surprise Reality check: Hyperscalers win on governance and security. Third-party tools win on deep debugging and operational reliability. Most enterprises will land on a hybrid: Hyperscaler runtime + open telemetry piping into specialized platforms. We must stop conflating model intelligence with system reliability. The competitive advantage belongs to those who wrap probabilistic cores in deterministic frame to force business-as-usual outcomes. Build the architecture of trust, or accept that your agents will remain impressive, unscalable liabilities. If you don’t build a trust architecture, your agents aren’t assets. They’re impressive liabilities. https://lnkd.in/g7R7nvXx #AgenticAI #AIEngineering #AIOps #Observability #Evaluation #Evals #OpenTelemetry #LLMOps #AITrust #EnterpriseAI #AIProductManagement #ReliabilityEngineering #ResponsibleAI #FinOps #DigitalTransformation EXL Rohit Kapoor Vivek Jetley Vikas Bhalla Anand Logani Baljinder Singh Anita Mahon Vishal Chhibbar Narasimha Kini Gaurav Iyer Shashank Verma Vivek Vinod Karan Sood Joseph Richart Aidan McGowran Saurabh Mittal Anupam Kumar Arturo Devesa Sarika Pal Adeel J. Pankaj Khera Vikrant Saraswat Wade Olson Puneet Mehra Arun Juyal Sarat Varanasi Naval Khanna Abhay B. Mustafa Karmalawala Akhil Saraf Anurag Prakash Gupta Nabarun Sengupta

🧠 Don’t Just Build AI Agents. Evaluate Them Ruthlessly. Everyone’s shipping agents. Few are measuring them. In the rush to integrate agentic AI into clinical operations, we’re missing a critical step: 👉 Evaluations — the disciplined, structured process of testing whether your AI actually delivers value. As Andrew Ng puts it, “Disciplined evals are the single biggest predictor of agentic AI progress.” Yet in life sciences, evaluations are often: 🫥 Vague 💭 Subjective 🧪 Done too late Let’s fix that. Here’s why it matters. 👇 💡 What is Agentic AI? Unlike single-shot prompts, Agentic AI chains together multiple steps, tools, or models to complete complex tasks. Think of them as junior team members with a task list and tools at hand. In clinical settings, these agents now support: ✍️ Medical writing and protocol drafting 📄 Document abstraction and QC 💬 Site communication bots 🧪 Lab data ingestion 📈 Feasibility analysis 🧍♂️ Patient concierge agents But if we don't evaluate their work like we would a new team member, we're flying blind. 🔍 Why Evaluations Are the Backbone of AI Readiness Let’s say your agent helps draft a clinical study synopsis. Great — but how do you know if it got the population, endpoint, or visit structure right? Without evaluations, you risk: ❌ Bad data entering downstream systems ❌ Increased human review costs ❌ Regulatory risk and rework ❌ False confidence in automation Evaluations act like clinical QA for your AI — a must-have, not a nice-to-have. Use a mix of: 🧑⚖️ Human spot checks 🤖 Automated schema checks 🧠 LLM-as-Judge evaluations 📌 Start early. Don’t wait until deployment — bake this into your prototype phase. 💥 Takeaways ✅ Agentic AI is only as strong as the evaluations behind it �� Don’t ship agents without defining what “good” looks like 🔬 Clinical use cases need contextual, field-aware evaluation plans 🧠 Focus on structured output, factual accuracy, and safety 📈 Better evals = faster iteration, lower risk, higher ROI 💬 Let’s Talk Are you evaluating your agents before you trust them? Drop your eval tactics, tools, or hard-won lessons in the comments. Let’s crowdsource the Agentic AI QA Playbook for our industry. 🏷️ Hashtags #AgenticAI #AIevaluations #ClinicalAI #GenerativeAI #ResponsibleAI

𝗪𝗵𝘆 𝟰𝟬% 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮𝗯𝗮𝗻𝗱𝗼𝗻𝗲𝗱 𝗯𝘆 𝟮𝟬𝟮𝟳? It’s not the agents. It’s not the tools. It’s the architecture. Agentic AI is the next frontier, systems where multiple autonomous agents plan, reason, and communicate to solve complex tasks. But many teams build agent demos in notebooks, then hit a brick wall trying to productionize. The real problem? Most agentic AI efforts start as fragile experiments without a solid engineering backbone. What goes wrong? 1️⃣ Protocol Chaos When agent-to-agent messages aren’t standardized, everything breaks. Successful teams use MCP (Model Context Protocol) and clean registries from day one. 2️⃣ Tool Fragmentation Hard-coding tools inside agents might work for a demo, but modular tool interfaces are critical for scale and future maintenance. 3️⃣ Missing Coordination Layer Multiple agents with no shared planner? That’s a recipe for confusion. A well-defined coordinator module is essential. 4️⃣ No Communication Bus Agent communication without a message bus quickly turns into spaghetti code. The solution? Architect for production on day one: - Clear separation of config - Modular tool orchestration - Robust communication protocols - Reasoning and planning layers Building agentic systems isn’t just prompt engineering. It’s designing a multi-agent architecture that can actually survive the real world. #AgenticAI #AIengineering #MCP #GenerativeAI

I have been developing Agentic Systems for the past few years and the same patterns keep emerging. 👇 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the most reliable way to be successful in building your 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 - here is my template. Let’s zoom in: 𝟭. Define a problem you want to solve: is GenAI even needed? 𝟮. Build a Prototype: figure out if the solution is feasible. 𝟯. Define Performance Metrics: you must have output metrics defined for how you will measure success of your application. 𝟰. Define Evals: split the above into smaller input metrics that can move the key metrics forward. Decompose them into tasks that could be automated and move the given input metrics. Define Evals for each. Store the Evals in your Observability Platform. ℹ️ Steps 𝟭. - 𝟰. are where AI Product Managers can help, but can also be handled by AI Engineers. 𝟱. Build a PoC: it can be simple (excel sheet) or more complex (user facing UI). Regardless of what it is, expose it to the users for feedback as soon as possible. 𝟲. Instrument your application: gather traces and human feedback and store it in an Observability Platform next to previously stored Evals. 𝟳. Run Evals on traced data: traces contain inputs and outputs of your application, run evals on top of them. 𝟴. Analyse Failing Evals and negative user feedback: this data is gold as it specifically pinpoints where the Agentic System needs improvement. 𝟵. Use data from the previous step to improve your application - prompt engineer, improve AI system topology, finetune models etc. Make sure that the changes move Evals into the right direction. 𝟭𝟬. Build and expose the improved application to the users. 𝟭𝟭. Monitor the application in production: this comes out of the box - you have implemented evaluations and traces for development purposes, they can be reused for monitoring. Configure specific alerting thresholds and enjoy the peace of mind. ✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: ➡️ Run steps 𝟲. - 𝟭𝟬. to continuously improve and evolve your application. ➡️ As you build up in complexity, new requirements can be added to the same application, this includes running steps 𝟭. - 𝟱. and attaching the new logic as routes to your Agentic System. ➡️ You start off with a simple Chatbot and add a route that can classify user intent to take action (e.g. add items to a shopping cart). What is your experience in evolving Agentic Systems? Let me know in the comments 👇

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐚𝐢𝐥 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞𝐲 𝐜𝐚𝐧 𝐧𝐨𝐭 𝐫𝐞𝐦𝐞𝐦𝐛𝐞𝐫 𝐂𝐨𝐧𝐭𝐞𝐱𝐭.  Here is the 10-step Roadmap to build Agents that actually work. From my experience,  successful deployments follow this exact progression: 1. Scope the Cognitive Contract • Define task domain, decision authority, error tolerance • Specify I/O schemas and action boundaries • Establish non-functional requirements (latency, cost, compliance) 2. Data Ingestion & Governance Layer • Integrate SharePoint, Azure SQL, Blob Storage pipelines • Normalize, chunk, and version content artifacts • Enforce RBAC, PII redaction, policy tagging 3. Semantic Representation Pipeline • Generate embeddings via Azure OpenAI embedding models • Vectorize knowledge segments • Persist in Azure AI Search (vector + semantic index) 4. Retrieval Orchestration • Encode user intent into embedding space • Execute hybrid retrieval (BM25 + ANN search) • Re-rank using similarity scores and metadata constraints 5. Prompt Assembly & Grounding • System instruction + policy constraints + task schema • Inject top-K evidence passages dynamically • Enforce source-bounded generation 6. LLM Reasoning Layer • Invoke GPT (Azure OpenAI) or Claude (Anthropic) • Tune decoding parameters (temperature, top-p, max tokens) • Validate deterministic vs creative response modes 7. Context & State Management • Persist conversational state in Azure Cosmos DB • Apply rolling summarization and relevance pruning • Maintain short-term and long-term memory separation 8. Evaluation & Calibration • Run adversarial, regression, and grounding tests • Measure hallucination rate, retrieval precision, latency • Optimize chunking, ranking heuristics, prompts 9. Productionization & Observability • Deploy via Microsoft Foundry and AKS • Implement distributed tracing, token usage, cost telemetry • Enable human-in-the-loop escalation paths 10. Agentic Capability Expansion • Integrate tool invocation (search, workflow, DB execution) • Add feedback-driven self-correction loops • Implement personalization via behavioral signals The critical steps teams skip: • Step 3 (Semantic Representation): Without proper vectorization, retrieval fails • Step 7 (State Management): Without memory persistence, agents restart every conversation • Step 8 (Evaluation): Without testing, hallucinations go to production My Recommendation: Don't skip steps. Each builds on the previous: • Steps 1-3: Foundation (scope, data, embeddings) • Steps 4-6: Core agent (retrieval, prompts, reasoning) • Steps 7-9: Production readiness (memory, testing, deployment) • Step 10: Advanced capabilities (tools, self-correction) Which step are you currently stuck on? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq

When AI Agents meet legacy systems.... It’s like millennials explaining Instagram to their Parents Lately, I’ve been having a lot of conversations around using multi-agent AI frameworks in legacy modernization projects and honestly, it’s one of the most exciting (and underrated) use cases of Agentic AI. Because let’s face it....legacy systems are like that old government building in our city: everyone knows it needs renovation, nobody knows where the wiring goes, and if you touch one file (or COBOL program), ten others mysteriously stop working. Here’s where multi-agent AI framework comes in and helps us out: --> System Discovery Agents – They can crawl through old documentation, codebases, and tickets to map what actually exists (since nobody’s quite sure anymore). --> Dependency Mapping Agents – Automatically identify what talks to what, and who’ll break if you change that one function. --> Knowledge Reconstruction Agents – Convert tribal knowledge (or “Ravi from Accounts’ memory”) into structured documentation. --> Refactoring Agents – Suggest and even execute modular migration strategies - rewriting parts of COBOL, Java, or .NET into modern microservices. --> Testing & Validation Agents – Auto-generate test cases, compare old vs new outputs, and flag anomalies before they reach production. This is the most important step, where human in the loop helps. The magic? Agentic AI isn’t just a “tool” here - it acts like a virtual project team that collaborates, plans, debates, and iterates… faster than humans could ever coordinate. Imagine 5 AI agents doing what used to take 50 consultants and 500 sticky notes and they don’t even need pizza breaks. Earlier, we had “legacy reengineering projects” that took years. Now, with Agentic AI, the legacy fears are finally being re-engineered. Do you have a similar experience?

Building agents is now commodity. Governing them is the moat. Gartner has put the number on it: over 40% of agentic AI projects will fail by 2027 without proper governance controls in place. The reason is not the technology. The reason is the architecture surrounding it. Here's the failure mechanism: Organizations that move fast on agentic deployment without governance infrastructure create three compounding problems. The Three Failure Modes: Failure Mode 1 — Agent Sprawl. Individual teams deploy agents independently. Each is a localized success. Together, they are a fragmented, insecure, duplicative architecture with no shared context and no accountability ownership. Every new agent added without governance increases technical debt exponentially — not linearly. Failure Mode 2 — Data Permeability. AI agents require access to systems, data, and APIs to function. Without identity governance and least-privilege enforcement, agents can access far more than their function requires. 37% of IT leaders name data privacy and security as their primary concern — and that concern is well-founded. Failure Mode 3 — ROI Diffusion. Without centralized measurement, agent ROI cannot be attributed. What cannot be attributed cannot be defended. And what cannot be defended gets cut. The Three Governance Foundations: Foundation 1 — Agent Identity Architecture. Every agent must have a defined identity, a scoped access permission set, and an owner. Foundation 2 — Centralized Orchestration. Multi-agent systems require coordination infrastructure — otherwise agents operate as silos. Foundation 3 — Audit-Ready Behavioral Monitoring. Governance is not a policy document. It is a real-time behavioral visibility system. The organizations scaling agentic AI without failure are not the ones who built the most agents first. They are the ones who built governance before they built the agents.

Agentic AI Is Not a Technology Shift. It’s an Operating Model Shift. There is growing excitement around Agentic AI, systems that can reason, plan, and take actions with limited human intervention. Most conversations, however, are centered on models, orchestration frameworks, and tooling choices. That focus is misplaced. Agentic AI does not primarily introduce a technology challenge. It introduces an operating model challenge. Traditional AI systems generate insights. Humans review those insights and make decisions. Accountability is clear, and risk is contained within existing governance structures. Agentic systems alter that boundary. When AI systems begin initiating actions, triggering workflows, making financial decisions, adjusting pricing, approving transactions, or interacting with customers, the locus of control shifts. The question is no longer about model performance alone. It becomes a question of authority, accountability, and economic exposure. As autonomy increases, the human role evolves. We move from directly executing decisions to supervising decision systems. That shift requires more than technical oversight. It requires deliberate design of institutional capability. If autonomous systems consistently perform operational tasks, organizations must ensure they retain the expertise, judgment, and muscle memory required to intervene effectively when those systems fail. Autonomy without capability retention creates dependency risk. Organizations adopting Agentic AI must therefore answer foundational operating questions:  - What categories of decisions can be delegated to autonomous systems?  - Under what financial or risk thresholds?  - Who retains override authority?  - How is action-level observability implemented?  - How are errors absorbed, remediated, and learned from?  - How is institutional knowledge preserved as automation scales? These are not engineering configuration issues. They are governance design decisions. Without clearly defined decision rights, trust tiers, and cost boundaries, autonomy scales faster than accountability. When that happens, risk scales faster than value. The organizations that will succeed with Agentic AI will not be those that deploy the most agents. They will be those that design a disciplined operating framework around autonomy, one that aligns technology capability with leadership intent, risk tolerance, and economic guardrails. Agentic AI is not simply about enabling systems to act. It is about deciding, with precision, what we are willing to let them decide. #AgenticAI #AILeadership #EnterpriseAI #OperatingModel #AIGovernance

Gartner says 40% of Agentic AI projects will fail by 2027 — A Step Toward Real Value As someone leading Intelligent Automation efforts, I’ve seen the pattern: a lot of agentic AI projects are launched not because the org is ready, but because the hype is loud, the pressure is high, and the promises are inflated. And then reality hits. 🔹 Brittle tech meets brittle workflows. 🔹 Hallucinations, poor decisions, and unpredictable errors stack up. 🔹 No ROI. No alignment. Just frustration. We’re expecting transformation, but deploying pilots with no foundations: no data readiness, no workflow redesign, no guardrails. This is not a tech issue. It’s a full-stack disruption: Business process, risk and trust, data infrastructure, workforce transformation. All of it needs to be in place before intelligent agents can truly deliver value. So as much as "40% failure" sounds alarming, I see it as a healthy filter. These aren’t lost opportunities; they’re course corrections. Many of those projects should never have been started in the first place. And yet… not all is doom and gloom. Behind NDAs and outside the spotlight, some teams are making progress, embedding agents into workflows where they quietly drive outcomes: financial close, supply chain escalations, risk mitigation, compliance workflows. Not copilots. Not demos. ✅ Real results. This is the stage we’re in now. Not the collapse phase, but what I’d call the integration phase. The “head-down, just-get-it-done” phase. As I look for the right Agentic AI opportunities, this is what I focus on: ➡️ Are we solving a real business problem, not just testing tech? ➡️ Is the data and trust architecture in place to support autonomy? ➡️ Are we prepared to redesign workflows, not just plug in a new tool? Because when agentic AI works, it quietly transforms. You see it in teams saving time, decisions getting faster, and quality improving. Let the hype burn off. What’s left will shape the next decade. If you’re working on this too, I’d love to hear: ➡️ Where are you seeing things work (or not)? ➡️ What’s been your biggest lesson so far? #AgenticAI #IntelligentAutomation #DigitalTransformation #FutureOfWork #AI #Gartner