AI AgentsThe MCP Security Model: Trust Boundaries You Actually Have
MCP standardises connection, not trust. Here are the real boundaries in an MCP deployment and the attacks that cross them, with practical mitigations.
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 Agents
AI AgentsTool Call Retries: Retry the Transport, Not the Judgement
Agents retry constantly, and most of it is wasted. How to tell a transport failure from a wrong decision, and what each one actually needs.
ReadAI AgentsTool Result Formatting: Shaping What the Model Reads Back
Tool output is the largest block of prompt content you never wrote. How to shape it so agents stay accurate, cheap and able to decide what to do next.
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AI AgentsTool Schema Design: The Prompt You Forgot You Were Writing
Tool definitions are prompt content the model reads on every turn. Design choices there change agent reliability more than model selection does.
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AI AgentsTracing Multi-Step Agents: Building a Trace Tree That Holds
A flat log of model calls cannot explain a twenty-step run. How to shape spans, propagate context through subagents and async tools, and replay a run from its trace.
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AI AgentsVerifier Agents: Gating Completion on a Check That Passes
The most reliable agent improvement is refusing to accept completion until something verifies it. How to build a verifier that is worth trusting.
ReadAI AgentsAgent Cost Control: Patterns That Bound the Worst Case
Agent spend is not a per-call problem, it is a per-loop problem. Budgets, caching, tiering and circuit breakers that cap what a bad run can cost you.
ReadAI AgentsAgent Error Recovery: Patterns That Keep the Loop Alive
Errors are the normal case in an agent loop, not the exception. These are the recovery patterns that separate agents that finish from agents that spiral.
ReadAI AgentsAgent Memory: Managing Context Before It Manages You
Long agent sessions fail because context fills with noise. Here are the practical strategies for deciding what an agent should remember and what to drop.
ReadAI AgentsAgent Observability: Tracing a Loop You Cannot Reproduce
Agent failures are rarely reproducible, so logs are not enough. What to record per step, how to span a tool loop, and which metrics predict a bad run.
ReadAI AgentsAgent Planning Strategies: From ReAct to Explicit Plans
Reactive loops drift on long tasks and rigid plans break on contact with reality. Here are the planning strategies that work, and how to choose between them.
ReadAI AgentsAgent Sandboxing: Containers, gVisor and microVMs
An agent that runs code needs a blast radius, not trust. How containers, gVisor and microVMs differ, and the network and credential controls that matter.
ReadShowing 49–60 of 74 articles