Building Autonomous AI Agents
Like Duolingo, but for Building Autonomous AI Agents. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
23 levels across 4 sections, about 46 minutes end to end, roughly 9 days at five minutes a day. It moves through The Core Agent Execution Loop; Planning, Control, and Verification; Multi-Agent Architectures; and Reliability and Production Engineering. It assumes you already know the basics.
Free forever · No credit card · iPhone & Android
Key ideas in Building Autonomous AI Agents
- The model reasons about the goal before deciding what tool to invoke.
- The agent emits a structured tool call to interact with an external system.
- The environment returns the tool's runtime output back into the prompt.
- Schema definitions trigger constrained decoding or targeted parameter extraction.
- Prose instructions allow ambiguous key naming and hallucinated parameter types.
- Structured validation catches parameter mismatches before execution occurs.
- Tool invocation errors must be packaged into the prompt as raw observation messages.
- Providing the exact parsing trace allows the model to alter its syntax in the next turn.
- Aborting the session on the first invalid call eliminates the opportunity to self-heal.
- Max iteration limits terminate the loop when an agent fails to conclude its task.
- Cycle detection flags identical repeated tool arguments that signal a logic stall.
- Temperature adjustments alter randomness but do not guarantee loop termination.
- Internal scratchpads store raw tool calls and payloads without polluting user chat
- Leaking raw execution traces into user history causes confusion and wastes context
- The conversation history only retains final sanitized agent summaries
- Indexing tool invocation sequences captures the functional logic of past tasks
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Building Autonomous AI Agents the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
How does an agent ask an outside computer program to do something?
Get it right to open this lesson and 22 more in the app.
Where Building Autonomous AI Agents takes you
Bridge the gap between basic prompting and production-grade agentic systems. Learn how to architect autonomous loops, manage memory, coordinate multi-agent topologies, and deploy reliable self-healing workflows.
- 1
The Core Agent Execution Loop
- The Execution Loop: ReAct and Beyond
- State Management and Memory Architectures
- 2
Planning, Control, and Verification
- Decomposition and Planning Strategies
- Human-in-the-Loop and Safety Guardrails
- 3
Multi-Agent Architectures
- Orchestration Topologies: Supervisors vs. Swarms
- Context Engineering and Dynamic Tool Retrieval
- 4
Reliability and Production Engineering
- Tracing and Observability for Agentic Runtimes
- Benchmarking, Evals, and Failure Analysis
4 sections · 8 units · 23 levels. Built to play, not to enroll.
You pick the voice
Building Autonomous AI Agents is taught in the Explain Like I'm 5 style: no big words. promise.. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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Start Building Autonomous AI Agents today.
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