Runtime software surrounding models that provides capabilities, state, execution, policy, orchestration, persistence, and observability.
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29 articles in this tag.
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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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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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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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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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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The demo works. The agent books the flight, summarizes the repo, files the ticket. Then you point it at a task with one extra branch, and it calls the same…
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Hop three. The context window is full. Half of it is plausible garbage — passages that matched the query, ranked well, and turned out to be useless. The…
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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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“Everything is a plugin” is a claim about architecture, not a feature list. It says the agent loop itself is replaceable — and that is expensive to mean.
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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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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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The agent worked yesterday. Today it burns forty thousand tokens before it reads your first instruction, and the hook that used to register identity with a…
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An agent processes 200 units of work over four hours, dies on unit 201, and restarts from zero. The crash is not the failure. The failure is that nothing…
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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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An agent that only works until the container restarts is not an agent. It is a demo with a heartbeat.
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Two projects both advertise "open source, model-agnostic, multi-channel, MCP support." A builder picks one, ships it, and discovers the real difference the…
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Your agent has thirty tools, four MCP servers, and a plan-mode toggle. It still fumbles a routine refactor, and when it does, you cannot tell whether the…
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An agent that plans beautifully and then executes a stale plan into a wall is not a planning failure. It is a state failure.
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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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An agent that acts without reasoning is not reasoning. It is guessing with a tool schema.
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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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The loop that improves the harness can also edit the evaluator, the permissions, and the rollback path. That is the whole problem in one sentence.
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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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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 works. Then you add a second worker, a shared artifact store, and a verifier, and the system starts producing results nobody can attribute,…
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A prompt is a spark, not an engine. The spark still matters — it just cannot carry the machine.
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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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