Agentic AI Engineer roadmap
Systems that decide what to do next, act on the world, and keep going without you watching. Everything worth learning to build them and keep them from causing damage.
17 stages265 topics
Assumes the AI engineer basics: Python, LLM APIs, prompting, structured output, and RAG. Section 1 lists the prerequisites - if most of it is unfamiliar, work through the general AI engineer roadmap first.
Prerequisites
Model Behaviour for Agents
Tool Design
Model Context Protocol
The Agent Loop
Planning and Decomposition
Context Engineering
Memory and State
Retrieval for Agents
Environments and Execution
Multi-Agent Systems
Safety, Permissions, Oversight
Evaluation
Observability and Debugging
Runtime and Infrastructure
Deployment and Operations
Professional Practice
Capstone
One agent that does real work on a real system, unattended, and can be trusted with it:
- A bounded, well-specified job
- Ten to twenty designed tools
- MCP servers for external systems
- A loop with iteration, time and cost limits
- Sandboxed code execution
- Durable state and resumability
- Compaction for long runs
- Approval gates on destructive actions
- Full audit trail of every action
- A trajectory eval suite that gates deploys
- Traces, cost and latency dashboards
- A documented rollback and kill switch
Then run it against a hostile evaluator. If prompt injection in the data it reads can make it take an action you didn't authorise, it isn't finished.
An agent that succeeds 90% of the time and fails silently is worse than one that succeeds 70% of the time and stops loudly. Most of the engineering here is in the second number, not the first.