Build tools, state, execution, authorization, recovery, and observability around AI models.
Tutorials
Explore the mechanisms, tradeoffs, and implementation patterns within this AI engineering track.
Two teams ship the same model, the same prompt, the same tool list. One agent finishes a multi-step task. The other stalls, loops, and quietly corrupts its…
Read tutorial
The demo worked. Production didn't. Somewhere between the two, the system took a path nobody wrote down, and when it failed there was no artifact…
Read tutorial
The demo agent works. It reads the file, runs the test, patches the bug. Then it ships to a second tenant, the session runs for six hours, the process dies…
Read tutorial
Two teams run the same model on the same task. One ships reliably. The other drowns in tool-call latency, context bloat, and incidents nobody can explain…
Read tutorial
A failed agent run rarely looks like an interface problem. It looks like a model problem: wrong tool, malformed arguments, an error swallowed and retried…
Read tutorial
Every agent builder hits the same wall. The first working agent is small: a system prompt, a few tools, a loop. Then someone needs release notes. Someone…
Read tutorial
A team wires up an MCP server, watches the tools appear in the client, and concludes the integration works. Then a tool gets renamed upstream, a resource…
Read tutorial
Kill a long agent mid-task and you learn which of those two things your system actually believes. If the process dies and the transcript survives but the…
Read tutorial
A fan-out graph looks fast on the whiteboard. In production it stalls because one branch is slow, one branch fails silently, and the synthesis step merges…
Read tutorial
A plan that reads like a clean numbered list can still deadlock at runtime, because the list never told the scheduler which steps actually depend on which…
Read tutorial
The demo worked because the agent was running on your laptop. It had your credentials, your network, your home directory, and your shell history. Move it…
Read tutorial
A research agent runs for twenty minutes. It pays for dozens of model calls, executes a dozen tool calls, and commits three external side effects. Then the…
Read tutorial
That is the shape of the failure I keep seeing in agent systems that look governed and are not. A team writes a planner prompt, a coder prompt, a reviewer…
Read tutorial
A user reports a wrong answer. You pull the trace. Every tool call succeeded, every span is green, latency is normal, and no error was logged anywhere. The…
Read tutorial
The demo works. The agent reads the queue, drafts the update, and posts it. Then someone asks who approved the change, which run produced it, and how to…
Read tutorial
The agent writes the function, re-reads it, decides it looks correct, and stops. The code is wrong. This is the most common failure in autonomous coding,…
Read tutorial