Miradi outlines 10-step agent playbook

AI agents are easy to demo and hard to ship. That’s the whole point of Maryam Miradi’s new playbook. In a March 5, 2026 video and follow-up posts, she laid out a 10-step stack for building agents that survive the stuff that usually kills them in production — hallucinations, prompt injection, memory drift, and orchestration mistakes. The interesting part is not that she named tools. It’s that she treated agent building like systems engineering, not prompt wizardry. (youtube.com) ### What did Miradi actually publish? She published a walkthrough called “How to Build AI Agents from Scratch in 2026 (Zero → Production Stack),” framed as a practical sequence rather than a theory lesson. The stack runs from business understanding and role design through structured I/O, grounding, reasoning, multi-agent orchestration, memory, deployment, observability, and evals. The companion post distills (youtube.com) one vague agent do everything, and don’t trust raw text outputs just because they look plausible. (youtube.com) ### Why start with business understanding? Because most agent failures are upstream. Miradi’s first move is not model choice. It’s understanding the workflow, the documents, and the constraints before any code gets written. She points to tools like NotebookLM and Google Gems as ways to turn messy source material into structured business context. That sounds boring next to “autonomous agents,” but turns out it(youtube.com)confidently automates the wrong task. (youtube.com) ### Why is structured I/O such a big deal? Because “please return JSON” is not a control system. Miradi pushes schema validation with PydanticAI so every input and output has a typed shape. That matters more than people admit. A lot of real breakage is not dramatic hallucination — it’s silent format drift, missing fields, or a tool call that almost matches what the downstream system expects. Her point is simp(youtube.com)etic problem. (youtube.com) ### Why bring in state graphs and multi-agent logic? Because once agents have tools, memory, and handoffs, a linear prompt stops being enough. Miradi uses LangGraph and CrewAI to separate roles and define how agents communicate. Think of it like replacing a group chat with an org chart. One agent gathers, another plans, another executes, and the graph controls who can talk to whom and when. That reduces overl(youtube.com)many responsibilities. (youtube.com) ### What’s the most concrete claim here? Two numbers stand out. Miradi says the stack is battle-tested across 400+ agents in 12 industries. She also says RAG for tool selection produced an 80% accuracy boost in her setup. Those are big claims, and they come from her own materials rather than an independent benchmark, but they show what she is optimizing for: not chatbot vibes, but measurable reductions in failure. (youtube.com) ### Why do memory and observability get their own steps? Because agents fail over time, not just on first response. Miradi describes a 9-type memory taxonomy and pairs deployment with monitoring tools like LangSmith and Langfuse. She even highlights three monitoring signals that caught a €200K hallucination after 11 days. That example is the real thesis of the whole playbook — if you are not tracing behavior (youtube.com)dent and too late. (youtube.com) ### Why end on evals and guardrails? Because an agent is not finished when it runs once. Miradi closes on layered testing — unit evals, integration evals, and adversarial evals. That puts guardrails at the end of the build, but really they shape the whole pipeline. If you know you must test tool use, memory, and edge cases later, you design the agent more cleanly from the start. (youtube.com)hift here is mental. Miradi is telling builders to stop treating agents like bigger prompts and start treating them like software systems with schemas, state, tests, and telemetry. That won’t make agents simple. But it does make them less fragile. (youtube.com)