Developing AI Agents

𝗧𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗦𝘁𝗮𝗶𝗿𝗰𝗮𝘀𝗲 represents the 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 from passive AI models to fully autonomous systems. Each level builds upon the previous, creating a comprehensive framework for understanding how AI capabilities progress from basic to advanced: BASIC FOUNDATIONS: • 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: The foundation of modern AI systems, providing text generation capabilities • 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Critical for semantic understanding and knowledge organization • 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Optimization techniques to enhance model responses • 𝗔𝗣𝗜𝘀 & 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀: Connecting AI to external knowledge sources and services INTERMEDIATE CAPABILITIES: • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Handling complex conversations and maintaining user interaction history • 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀: Short and long-term memory systems enabling persistent knowledge • 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 & 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: Enabling AI to interface with external tools and perform actions • 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: Breaking down complex tasks into manageable components • 𝗔𝗴𝗲𝗻𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: Specialized tools for orchestrating multiple AI components ADVANCED AUTONOMY: • 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: AI systems working together with specialized roles to solve complex problems • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: Structured processes allowing autonomous decision-making and action • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: Independent goal-setting and strategy formulation • 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴: Optimization of behavior through feedback mechanisms • 𝗦𝗲𝗹𝗳-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜: Systems that improve based on experience and adapt to new situations • 𝗙𝘂𝗹𝗹𝘆 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜: End-to-end execution of real-world tasks with minimal human intervention The Strategic Implications: • 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻: Organizations operating at higher levels gain exponential productivity advantages • 𝗦𝗸𝗶𝗹𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Engineers need to master each level before effectively implementing more advanced capabilities • 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹: Higher levels enable entirely new use cases from autonomous research to complex workflow automation • 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀: Advanced autonomy typically demands greater computational resources and engineering expertise The gap between organizations implementing advanced agent architectures versus those using basic LLM capabilities will define market leadership in the coming years. This progression isn't merely technical—it represents a fundamental shift in how AI delivers business value. Where does your approach to AI sit on this staircase?

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

🌟 New Paper: AI Agents vs. Agentic AI Interesting paper summarizing distinctions between AI Agents and Agentic AI. It also talks about the key ideas, solutions, and the future. Here are my notes: ⚪ What is the paper about? The paper provides a comprehensive taxonomy and comparison between AI Agents and Agentic AI, clarifying their conceptual, architectural, and operational differences. ⚪ What are AI Agents? AI Agents are single-entity systems enhanced with LLMs and external tool integration, capable of task-specific autonomy and sequential reasoning. They are reactive, modular, and typically used for narrow applications like email triage, scheduling, or customer service. ⚪ What is Agentic AI? Agentic AI represents an architectural shift. These systems involve multiple collaborating agents with dynamic task decomposition, persistent memory, and orchestration layers. They enable higher-level coordination and are suited for complex workflows like research automation, robotic swarms, and medical diagnostics. ⚪ Application Mapping AI Agents: Email filtering, report summarization, content recommendation, customer support. Agentic AI: Coordinated research assistants, ICU decision support, robotic orchard harvesters, adaptive game AIs. ⚪ Challenges AI Agents: Limited causal reasoning, hallucinations, lack of proactivity, brittle long-horizon planning. Agentic AI: Inter-agent error cascades, emergent instability, opaque communication, scalability, explainability, and security vulnerabilities. ⚪ Key Architectural and Algorithmic Solutions - Retrieval-Augmented Generation (RAG) - Tool-augmented reasoning (function calling) - Agentic Loop: Reasoning, Action, Observation - Memory Architectures (Episodic, Semantic, Vector) - Multi-agent orchestration with Role Specialization - Reflexive and Self-Critique Mechanisms - Programmatic Prompt Engineering Pipelines - Causal Modeling and Simulation-based Planning - Monitoring, Auditing, and Explainability Pipelines - Governance-aware design with role isolation and traceability There are all important areas that researchers and developers need to get familiar with to build reliable and robust agentic systems. ⚪ Future Roadmap For AI Agents: Proactive intelligence, continuous learning, trust & safety. For Agentic AI: Multi-agent scaling, simulation-based planning, ethical governance, and domain-specific systems. These are all areas that need huge innovations in algorithms, architectures, infrastructure, protocols, and enhancing the models themselves.

AI Agent vs Agentic AI Most people use the terms AI Agent and Agentic AI like they mean the same thing. They don’t. The difference isn’t just semantic. It’s architectural. Here’s how the tech stack evolves from AI Agent → Agentic AI 👇 1. Intelligence models - AI Agent typically relies on a single LLM with prompt → response workflows. - Agentic AI moves toward multi-model reasoning, planner–executor setups, and hybrid inference across systems. 2. Architecture & frameworks - AI Agent often follows a single-agent, linear execution flow. - Agentic AI introduces multi-agent systems, goal-driven workflows, and orchestration frameworks like LangGraph, CrewAI, or AutoGen. 3. Memory systems - AI Agent works with session memory, short-term embeddings, and basic caches. - Agentic AI adds long-term memory layers, episodic + semantic memory, knowledge graphs, and vector databases. 4. Tool usage & actions - AI Agent uses predefined tools and function calling triggered by users. - Agentic AI autonomously selects tools, plans multi-step executions, interacts with environments, and uses structured tool registries. 5. Knowledge & retrieval - AI Agent typically uses basic RAG pipelines with static retrieval. - Agentic AI evolves into adaptive RAG, context prioritization, hybrid search, and continuously updated knowledge graphs. 6. Orchestration & workflows - AI Agent runs sequential flows and simple backend automation. - Agentic AI uses orchestration engines, planning loops, event-driven workflows, and reflection cycles. 7. Decision making - AI Agent is reactive and prompt-driven. - Agentic AI is goal-oriented, with planning, self-evaluation, and iterative reasoning loops. 8. Deployment - AI Agent is often deployed as chatbots, copilots, or API-based assistants. - Agentic AI becomes autonomous platforms, digital workforce agents, and persistent execution systems. 9. Monitoring & observability - Both need logs, monitoring, and error tracking but Agentic AI requires deeper analytics, response monitoring, and system-level feedback loops. 10. Learning & improvement - AI Agent improves through prompt iteration and occasional fine-tuning. - Agentic AI evolves through continuous feedback pipelines, performance adaptation, and evaluation frameworks. AI Agent = intelligent responder. Agentic AI = autonomous system with goals, memory, tools, and orchestration. One answers questions. The other executes objectives. Are you building smarter responses or autonomous systems?

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

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

If you’re getting started in the AI engineering space and want to understand how to actually build an AI agent, here’s a structured way to think about it. Over the last several months, I’ve been building, testing, and teaching agentic AI systems, and I realized most people jump straight into frameworks like LangGraph, CrewAI, or AutoGen without fully understanding the system design mindset behind them. Here’s a 12-step framework I put together to help you design your first AI agent, end-to-end. 🧩 From defining the problem to scaling it reliably. → Start with Problem Formulation & Use Case Selection - clearly define the goal and validate that it needs agentic behavior (reasoning, tool use, autonomy). → Map the User Journey & Workflow - understand where the agent fits into human or system loops. → Build your Knowledge & Context Strategy - design a RAG or memory pipeline to give your agent structured access to information. → Choose your Model & Architecture - open-source, fine-tuned, or multimodal depending on the use case. → Define Agent Roles & Topology - whether it’s a single-agent planner or a multi-agent ecosystem. → Layer on Tooling & Integration - secure APIs, function calling, and monitoring. → Then move into Prototyping, Guardrails, Benchmarking, Deployment, and Scaling - optimizing for accuracy, latency, and cost. Each layer matters because building an AI agent isn’t about wiring APIs, it’s about engineering autonomy with accountability. Now that you have this template, pick a use case that excites you - maybe something that improves your own productivity or automates a workflow you repeat daily. Or look online for open project ideas on AI agents, and just start building. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Most AI agents today have the same frustrating flaw: They don’t learn. You correct them… and they repeat the exact same mistake at the next task. You show them the right workflow… and it disappears the moment the session ends. Last week, I came across an open-source project that actually fixes this. And honestly, it changes how we think about agent architecture. It’s called Acontext and it gives AI agents the one thing they’ve always lacked: 👉 The ability to learn from real tasks and turn them into reusable skills. What impressed me the most Acontext doesn’t just store messages. It builds a full learning loop around every single task your agent performs. In plain English, here’s what it does: 1️⃣ Store Captures persistent context, session history, and artifacts like a memory layer that never resets. 2️⃣ Observe Watches how the agent solved a task, including tool calls, user feedback, and intermediate steps. 3️⃣ Learn Extracts those steps → identifies patterns → turns them into SOP-style skill blocks. These skill blocks then live inside a Notion-like workspace, ready to be reused whenever a similar task appears. Your agent doesn’t just respond… It remembers and improves. The architecture is genuinely smart: User ↕ Your Agent ↕ Session (stores all messages & artifacts) ↓ Task Extraction ↓ Task Completion ↓ Skill Learning ↓ Skill Blocks (saved) ↓ Search → Reuse → Improve This is the closest I’ve seen to a practical “self-learning” agent system. Multi-modal support is already built in: ✓ Text ✓ Images ✓ Files ✓ Tool calls ✓ OpenAI format ✓ Anthropic format Basically… if your agent can see it, Acontext can learn from it. Completely open-source. Apache 2.0. Free. While some companies pay $200/seat for static enterprise chatbots, you can now build self-improving agents without spending a rupee. And yes Python & TypeScript SDKs are already available. GitHub → https://lnkd.in/gS5rJbit If you’re building AI agents, this is one of the most important repos to watch right now.

Gartner said 40% of agentic AI projects will be cancelled by 2027. Everyone calls it a model problem. It isn't. We are not getting closer to 2027. The model was the least broken thing in the system. Almost every time, I found the real failures cluster in 6 buckets. Only 1 is about the LLM. Here are these buckets: 1️⃣ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: The agent hallucinates because the source of truth doesn't exist. Not because the model is bad. 2️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁 & 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻: Brittle prompts. No versioning. No regression suite. Output drifts. Nobody can tell you why. 3️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Yes, this is real. But it's #3. Not #1. 4️⃣ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶on: Tool calls fail silently. Steps run out of order. The "agent" is a Rube Goldberg machine wearing autonomy as a costume. 5️⃣ 𝗘𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗼𝗴𝗶𝗰: The agent doesn't know when it doesn't know. It keeps plowing through tasks that should have gone to a human three steps ago. Almost like an “ego” 6️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗚𝗮𝗽: You can't tell if the agent got better or worse this week. So you can't fix anything systematically. You just patch. Calling this "a model problem" lets a lot of people/processes off the hook. The LLM vendor sells you the next model. The platform team avoids the orchestration debt. Leadership keeps funding pilots that were architecturally doomed on day one. Agent drift is the new model drift. It's a lifecycle problem. Not a capability problem. You don't fix it by upgrading the model. You fix it by building the system around the model. #ExperienceFromTheField #WrittenByHuman

Every company has an "AI strategy" now. But 90% suck. Here's step-by-step how to build one that doesn't: AI strategy is different from regular product strategy. This is the battle-tested framework Miqdad Jaffer & I use. We've used at Shopify, OpenAI, & Apollo: — 1. SET CLEAR OBJECTIVES At Shopify, Miqdad killed dozens of technically cool AI projects... And doubled down on inventory management. Why? That’s where merchants were losing money. No business impact = no AI initiative. Simple as that. Look for pain points humans consistently fumble, impact their growth, and first solve that with AI. — 2. UNDERSTAND YOUR AI USERS Users don’t adopt AI the same way they adopt a button or a new flow. They don’t JUST use it. They test it, build trust with it, and only then rely on it. So, build something that empowers them throughout their journey with your product. — 3. IDENTIFY YOUR AI SUPERPOWERS Not everyone has access to the same behavior signals... User context, or proprietary data that make outputs smarter over time. That’s your moat, the data nobody else can use. Not the fancy models. Not the MCPs. Not even revolutionary AI agents. Your goal is to build around your moat, not your product or models. — 4. BUILD YOUR AI CAPABILITY STACK In AI, speed beats pride. Think of it this way: A team spends 9 months building their own LLM. Meanwhile, a smaller competitor ships with OpenAI and captures the market. So, did you make the smartest move by trying to build everything yourself? Great PMs lead when to build and when just to leverage. — 5. VISUALIZE YOUR AI VISION In 2016, Airbnb used Pixar-level storyboards to communicate product moments. Today? Tools like Bolt, v0, and Replit make it possible in hours for a fraction of a cost. Create visiontypes that show: → Before vs. after (and make the “after” impossible to do manually) → Progressive learning and smarter experiences → Human + AI collaboration in real workflows — 6. DEFINE YOUR AI PILLARS At this stage, you’re building a portfolio of some safe and some big bets: → Quick wins (1–3 months) → Strategic differentiators (3–12 months) → Exploratory options (R&D, future leverage) And label each one clearly: Offensive = creates new value Defensive = protects from disruption Foundational = unlocks future bets — 7. QUANTIFY AI IMPACT If your AI strategy assumes flat, linear returns - you’re modeling it wrong. AI compounds with usage. Every interaction trains the system, feeds the flywheel, and lifts the entire product. Even Sam Altman shared that just adding a “thank you” feature increased OpenAI’s operational cost by millions.... — 8. ESTABLISH ETHICAL GUARDRAILS One biased result. One hallucination. One misuse. And the entire product feels unsafe. Set guardrails around every part of the process to make it safe... From all the hallucinations that disrupt your trust! — Making a great strategy is still hard. But these steps can help.