Build the software layer around the model with tools, skills, MCP, state, execution, authorization, recovery, persistence, and observability.
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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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…
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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,…
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