Build a Mission Control for AI Agents
AI agents are moving out of the chat window and into the work: issues, docs, schedules and approvals. Teams that wire agents straight into their tools lose track of what ran, who approved it and what it cost. Live, I build a mission control board where agents with roles pick up GitHub and Linear work, ask for approval on Telegram and log every run with its cost.
The lessons, in order
Each lesson is one idea from the live build, cut to a few minutes, with chapters and the full transcript. The first is free once you sign in; Pro opens the rest.
- 1Free6:51
Verify What Your AI Agent Calls Done
How do you verify what your AI agent calls done? Mission control, as Param uses the term, is anything that checks what an agent produced: whether the task is actually done or the agent only says it is. After this lesson you can choose between AI checks, deterministic checks and a human in the loop, and see why verification is the bottleneck.
- 23:46
An AI Agent Team With a Mission Control Board
How do you build an AI agent team with a mission control board? In this demo a made-up company, Acme, gets bug reports on GitHub and three agents handle them: a manager that classifies and supervises, a support agent that drafts the reply, and an engineering agent that reads the docs. After this lesson you can clone the demo repo, run it with a mock run or through OpenRouter, and define the boundaries each agent needs.
- 38:00
Human Approval Belongs in Code, Not the Prompt
The support agent drafts a reply and waits for a human to approve it. Why that approval cannot be a line in a prompt or skill, since a small model follows it only most of the time, and how a check in code plus traces shows who approved and what the agent read.
- 46:41
Done Is Decided by a Check, Not the Agent
What decides that an AI agent's task is done? An LLM can say it finished; a deterministic check decides whether it did. In the demo, done is decided by GitHub showing the reply. After this lesson you can control code at the source with skills and formatting checks instead of line-by-line review, and use a failed check to send the agent back.
- 54:45
Cap AI Agent Loops With Cost and Max Turns
How do you cap AI agent loops with cost and max turns? Max turns is how many times an agent may loop on a task; a cost cap is what a run may spend. Param prefers to set both as an agent's boundary. After this lesson you can set those two limits and track cost per outcome, the number to watch on each release.
What the course covers
Engineers who have built an agent and want it doing real work for their team, and the tech leads designing how agents fit into their company's tools.
- Build a board your AI agents work from
- Backlog, Needs you, In flight and Done, with each agent's role and tools.
- Ask a human before anything risky
- An agent pauses and asks on Telegram before an action it cannot undo.
- Map the design onto your own company
- Swap the connectors for the tools your team already uses.
More video courses
Every Lightning session becomes one, in the order they ran.
Build it live, with Param reviewing your choices
You are on Build a Mission Control for AI Agents
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- ProWatch the video coursesDesign a Software Factory for Your Coding Agents, Build Your First Software Factory Execution Harness and each new course as the sessions are cut. The first lesson of each is free after you sign in.$149 a year
- WorkshopAI Agents in Production for EngineersA multi-agent system deployed with an identity per agent, permissions, guardrails, an LLM gateway, traces and evals, plus a checklist for your own agents.$497
- BuildcampAgentic AI Buildcamp for Engineers & PMsDesign, build and deploy a production agentic AI system in four weeks$997