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

The AI landscape has shifted. We are moving away from models that just produce output (text, images, code) to systems that produce outcomes (executing tasks, solving problems, and collaborating). If you’re still thinking of LLMs as just "search engines with a personality," you're missing the bigger picture: Agentic AI. This roadmap breaks down the entire ecosystem into a digestible path. Here’s a high-level look at what you need to master: 1. The Core Shift: Output vs. Outcome Generative AI responds to prompts. Agentic AI perceives, reasons, plans, and acts. It’s the difference between asking for a travel itinerary and having an agent actually book the flights, handle the cancellations, and sync your calendar. 2. The Tech Stack Building an agent requires more than just an API key. You need: Reasoning Loops: ReAct, Chain-of-Thought, and Self-Correction. Memory Systems: RAG (Retrieval-Augmented Generation) for long-term "semantic" memory. The Execution Layer: Giving AI the "hands" to use tools—Python workers, APIs, and browser actions. 3. Frameworks to Watch Don’t reinvent the wheel. Frameworks like LangGraph, CrewAI, and Microsoft AutoGen are becoming the industry standards for orchestrating multi-agent workflows. 4. Multi-Agent Systems (MAS) The future isn't one giant "god-model." It’s a team of specialized agents—Planners, Researchers, Coders, and Critics—working together, debating, and reaching consensus to finish complex projects. How to Start Building? Define the Goal: What specific outcome do you want? Decompose: Break that goal into smaller, manageable tasks. Implement Guardrails: Security and observability are non-negotiable for autonomous systems. Evaluate: Use tools like Ragas or LangSmith to measure success beyond just "it looks right." 2026 is the year of the Agentic Workflow. It’s no longer about who can write the best prompt, but who can build the best system. Which part of the Agentic stack are you focusing on this year? Reasoning, Tool-use, or Multi-agent orchestration? Let’s discuss in the comments!

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

The EU AI Act demands technical teeth; are we ready yet? I’ve been digging into the COMPL-AI Framework paper from ETH Zürich and LatticeFlow AI, and it crystallizes something I’ve been saying in my AI governance work: regulation without measurable technical standards is just aspiration on paper. The researchers built the first comprehensive technical interpretation of the EU AI Act for LLMs; translating broad regulatory language into 27 concrete benchmarks across robustness, privacy, fairness, transparency, and safety. Then they evaluated 12 prominent models including GPT-4, Claude 3, and Llama 3. The verdict? No model is fully compliant. Not one. Three findings that should concern every builder deploying AI in regulated environments:- 1. Capability ≠ Compliance. Models that score well on knowledge and reasoning benchmarks still fail on fairness, robustness, and traceability. Qwen1.5-72B scores 0.71 on capabilities but just 0.37 on fairness. We’ve been optimizing for the wrong things. 2. Our benchmarks have blind spots. Current privacy and copyright evaluations are too simplistic to be meaningful; most models score near-perfect not because they’re compliant, but because the tests can’t detect violations. Explainability? No adequate technical benchmark even exists yet. 3. Small models carry disproportionate risk. Smaller LLMs consistently underperform on robustness and cyberattack resilience. As organizations rush to deploy lightweight models for cost efficiency, they may be trading compliance for convenience. For those of us building agentic AI systems, this has profound implications. When autonomous agents chain multiple LLM calls together, these individual model gaps compound. A fairness score of 0.50 at the model level becomes a systemic risk at the orchestration level. This is exactly why at COHUMAIN Labs, our Joint Evaluation (Jo.E) framework integrates this kind of regulation-aligned benchmarking directly into users’ agentic AI workflows, combining LLM-as-a-judge, specialized AI agents, and human expert validation in a tiered assessment structure so compliance isn’t an afterthought but is embedded by design. Where COMPL-AI maps the “what” needs to be measured, Jo.E operationalizes the “how” for teams building and deploying agentic systems in the real world. What excites me about COMPL-AI is that it proves regulation-aligned benchmarking is possible, even if imperfect. It’s the kind of bridge between policy intent and engineering practice that the GPAI Code of Practice desperately needs. The EU AI Act enforcement deadlines are approaching. The question isn’t whether your models will be evaluated against technical standards; it’s whether you’ll be ready when they are. Full paper:- arxiv.org/abs/2410.07959 Open-source suite:- compl-ai.org #AIGovernance #EUAIAct #ResponsibleAI #LLM #AgenticAI #AICompliance #JoE #COHUMAIN

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?

AI agent evaluation sounds way more complicated than it actually is. Most teams struggle because they confuse single-turn agents (one interaction to complete a task) with multi-turn agents (multiple back and forth exchanges). This leads to applying the wrong metrics in the wrong places. Here's what actually matters for evaluating any AI agent: ➡️ Trace your agent's execution flow using LLM observability. You need to see what's happening at each component before you can measure it. ➡️ Apply metrics at two levels. Component level catches tool call failures and parameter issues. End to end level catches task completion failures. ➡️ Start with 3 to 5 core metrics. Task completion for whether the agent works. Argument correctness for whether tools are called correctly. Custom metrics for your specific use case. The mistake is trying to evaluate everything at once. Pick the failure mode most likely to break your agent, trace that component, measure it with the right metric. What's the biggest failure mode you're seeing in your AI agents right now?

𝐈𝐟 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞… 𝐰𝐡𝐲 𝐝𝐨 80% 𝐨𝐟 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐬𝐭𝐢𝐥𝐥 𝐬𝐭𝐫𝐮𝐠𝐠𝐥𝐞 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲 𝐞𝐯𝐞𝐧 𝐨𝐧𝐞 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥𝐥𝐲? Because most teams focus on building the agent, not on building the system the agent needs to survive. That’s where the 7 𝑭𝒐𝒖𝒏𝒅𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝑷𝒊𝒍𝒍𝒂𝒓𝒔 𝒐𝒇 𝑨𝒈𝒆𝒏𝒕𝒊𝒄 𝑨𝑰 come in. This framework is becoming non-negotiable for anyone designing real, production-grade agents. Most orgs today are stuck at “prompt → response”. But enterprise-grade agents require something far more structured — the same pillars that power Copilot, Azure AI, and modern distributed AI systems. Here’s what actually separates “just another agent demo” from a scalable Agentic AI capability 👇 1️⃣ 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 & 𝐓𝐚𝐬𝐤𝐬 — The Brain of the Agent Goal setting, chain-of-thought planning, multi-agent coordination, dynamic re-prioritization. ➡️ Without this, your agent can’t think — it can only react. 2️⃣ 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 & 𝐓𝐚𝐬𝐤𝐬 — The Agent’s Long-Term Intelligence Vector databases, episodic recall, semantic indexing, and temporal awareness. ➡️ 78% of agent failures come from missing or inconsistent memory. 3️⃣ 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 — Turning Instructions Into Action Tool invocation, API calling, multi-step reasoning, and autonomous action. ➡️ This is where agents stop “chatting” and start doing. 4️⃣ 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 — The Backbone of Reliability Health checks, error detection, audit logs, real-time tracking, dashboards. ➡️ If you can’t observe it, you can’t trust it. 5️⃣ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 — How Agents Learn & Improve Reinforcement learning, feedback loops, policy updates, and self-adaptation. ➡️ AI that doesn’t learn… becomes a liability. 6️⃣ 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 — The Engine Room Vector DBs, GPUs, cloud hosting, Kubernetes, API gateways. ➡️ Agents aren’t “lightweight”. They need real engineering. 7️⃣ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 — The Guardrails Ethical constraints, risk checks, RAI, bias detection, and access control. ➡️ As Satya Nadella keeps saying: AI must be useful AND safe. 💬 The question every leader should be asking in 2025: “𝘈𝘳𝘦 𝘸𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢𝘨𝘦𝘯𝘵𝘴… 𝘰𝘳 𝘢𝘳𝘦 𝘸𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘵𝘩𝘦 𝘦𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮 𝘵𝘩𝘢𝘵 𝘢𝘭𝘭𝘰𝘸𝘴 𝘢𝘨𝘦𝘯𝘵𝘴 𝘵𝘰 𝘸𝘰𝘳𝘬?” If you’re serious about AI readiness, start with these pillars. This is the architecture that will separate future-proof companies from the ones still experimenting.