Maryam Miradi publishes 10 steps

AI agents are moving out of demo land and into actual work systems. That sounds obvious, but the gap has been huge — lots of people can make a chatbot, far fewer can ship something reliable enough for a team to use every day. Maryam Miradi’s 10-step roadmap is useful because it treats an agent less like a clever prompt and more like a software system with interfaces, memory, tools, monitoring, and failure modes. That’s the real shift here. ### What did she actually publish? She laid out a step-by-step build order for AI agents — define the role, structure the inputs and outputs, set the prompt protocol, add reasoning and tools, decide whether you need multiple agents, add memory, optionally add voice or vision, package the output, wrap it in a UI or API, then evaluate and monitor it. That sounds simple, but the sequence of “prompt and pray.” ### Why does the sequence matter? Because most agent failures start upstream. If you don’t define the agent’s job clearly, the rest gets mushy. If you don’t define structured inputs and outputs, every downstream step becomes brittle. Miradi explicitly pushes JSON schemas and Pydantic-style validation early, which is basically a way of saying: stop treating production agents like free-form chat and start treating them like APIs. ### Why are structured I/O and protocols such a big deal? They make the system predictable. A good agent is not just “smart.” A good agent returns the right fields, calls the right tools, and hands work to the next component without breaking format. That is why her roadmap puts schema design and protocol definition before fancy orchestration. Turns out multi-agent graphs are not the hard part in the first place. ### Where do reasoning and tools fit? In the middle — not at the start. Her stack adds reasoning patterns like ReAct and then gives the agent access to tools such as search, code execution, or retrieval. That ordering matters because tool use without boundaries becomes chaos fast. In the newer video version of the framework, she expands this into a much heavier production stack — 17 reasoning and planning methods, plus retrieval tuned for tool selection. ### Why bring in multiple agents at all? Only when the work really splits into roles. Her examples are planner, researcher, and reporter agents, each with separate schemas and coordination logic. That is a more disciplined version of the multi-agent trend — not “more agents because it sounds advanced,” but separate agents because role isolation can reduce confusion and make handoffs auditable. She points to CrewAI, LangGraph, and OpenAI Swarm for that layer. ### What about memory, voice, and vision? This is where the roadmap starts to feel like a workplace blueprint rather than a toy tutorial. She includes conversational, summary, and vector memory, then adds optional multimodal layers — text-to-speech with ElevenLabs or Coqui, and image understanding with GPT-4o or LLaMA vision models. In her broader training materials, that same stack extracts data. ### Why end on evaluation and monitoring? Because that is the part the hype cycle keeps skipping. Miradi closes with logs, benchmarks, feedback loops, and monitoring, and the newer production version goes further with unit, integration, and adversarial evals plus observability tools like LangSmith or Langfuse. Basically, the roadmap says an agent is not finished when it answers once. It's getting better. ### So what’s the real takeaway? The useful idea here is not any single tool. Tools will change. The durable part is the frame: role, schema, protocol, tools, orchestration, memory, interface, evals, monitoring. That is the difference between an agent demo and an agent product — and more people in AI are finally building like that.