AI AgentsApproval Gates in Agents: Where to Put the Human
Asking a human to approve everything trains them to approve nothing. How to place gates so the few that remain still get read carefully.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Agents, tool calling, and the loop that makes them useful.
AI AgentsAI AgentsBuilding an MCP Server: A Working One in an Afternoon
A practical walkthrough of writing your first MCP server: choosing the tools, wiring the transport, handling errors, and testing it with a real agent.
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AI AgentsContext Compaction: Keeping Agents Alive Past Turn Twenty
Agent transcripts grow until quality degrades and cost climbs. What to summarise, what to drop, and what must never be compacted away.
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AI AgentsCritic Agent Patterns: Useful Review Without Endless Rewrites
A critic agent reviews work another agent produced. The patterns that improve output, the ones that just add cost, and how to tell them apart.
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AI AgentsDebugging a Stuck Agent: A Triage Runbook
Stuck is four different failures with four different fixes. How to tell hung from looping from stalled from quietly truncated, and what to do about each one.
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AI AgentsDependency-Aware Planning: Ordering Work That Blocks
Most agent plans are flat lists executed top to bottom. Modelling dependencies instead unlocks parallelism, better retries and honest progress reporting.
ReadAI AgentsDetecting Agent Loops Before They Burn Your Budget
A stuck agent repeats the same failing action until something stops it. How to detect repetition cheaply and what to do once you have.
ReadAI AgentsIdempotency in Agent Actions: Safe to Repeat by Design
Agents retry, resume and duplicate calls constantly. Idempotency keys, natural keys and check-then-act patterns that stop one action happening three times.
ReadAI AgentsMap-Reduce With Agents: Splitting Work That Actually Splits
Map-reduce over an LLM is not the same as map-reduce over data. Where the shape works, where the reduce step quietly loses information, and how to fix it.
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AI AgentsMCP Server Design: Fewer Tools, Sharper Boundaries
Most MCP servers are a thin wrapper around an existing API, which is why agents use them badly. Design rules for servers models can actually operate.
ReadAI AgentsMCP Transport Options: stdio, HTTP and When Each Fits
MCP runs the same JSON-RPC messages over a local subprocess or an HTTP endpoint. The transport you pick decides your auth, scaling and debugging story.
ReadAI AgentsMCP vs Plain Tool Calling: When the Protocol Earns Its Keep
MCP and a plain function schema look identical to the model. The difference is who owns the integration, and that decides which one you should use.
ReadShowing 25–36 of 74 articles