ai-platform/roadmap
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Start here

  1. How to use this roadmap
  2. Two kinds of role
  3. Read code, write it rarely
  4. Target profile
  5. Eight principles
  6. Work on a real product

Phases

  1. 01Direct coding agents and read their code
  2. 02Learn how models behave and what they cost
  3. 03Build retrieval that finds the right evidence
  4. 04Build tool servers with MCP
  5. 05Design agent workflows that survive failure
  6. 06Run agent workloads on AWS
  7. 07Trace every step an agent takes
  8. 08Build evals that catch regressions
  9. 09Run agents reliably in production
  10. 10Secure agents and the code they write
  11. 11Build an internal AI platform
  12. 12Get teams to adopt the platform
  13. 13Build Forge, the capstone platform
  14. Elective: Kubernetes

After the phases

  1. Staff skills
  2. 12-month plan
  3. A typical week
  4. Show your work
  5. Tools to know
  6. What to skip
  7. Final checklist

Start here

Eight principles behind the roadmap

Every phase follows these eight principles.

Put your effort into checking code, not producing it

An agent can write a feature in minutes. The hard part is knowing whether the feature is correct, safe, and worth shipping. Spend more effort on tests, types, evals, traces, and review than on generating code.

Own what you merge

The person who merges a change is responsible for it, whoever wrote it. If you cannot explain why a change is safe, do not merge it.

Learn the pattern before the tool

Learn what a router, a durable workflow, a retry policy, and an eval loop are before you use a library that provides them. The same goes for agent tools. The terminal tool you use this quarter will change. The need to isolate and watch several agents will not.

Put hard limits around the model

Model output varies from run to run. Permissions, budgets, stored state, audit logs, and failure handling must not vary.

Turn every production failure into a test

Capture the failure as a trace, add a representative case to the eval set, fix the system, and let CI stop the failure from coming back.

Limit what an agent can do

A model, including your own coding agent, can take only the actions that the system around it allows. Autonomy is a permissions problem as much as an intelligence problem.

Treat cost as part of reliability

A workflow that succeeds but spends an unpredictable amount on tokens and tools is not ready for production.

Make the safe choice the default

Teams should not have to learn your platform to use it safely. Build it so that the default choice is also the safe one.