Developer Tools & AI Engineering
Articles
9Idempotency Keys for Agent Tool Calls: Making Retries and Replays Safe
Agents retry, resume and repeat themselves. Here is how idempotency keys, modeled on payment APIs, keep side-effecting tool calls safe.
Designing a Multi-Provider Model Router: Fallback Chains, Not Just a Single Switch
Routing between model providers for cost or redundancy sounds like a load-balancer problem. The failure modes say otherwise.
When to Use a State Machine Instead of a Prompt for Agent Control Flow
Deterministic transitions belong in code, not in prose. Here is how Anthropic's workflow patterns help you decide what the model should and shouldn't control.
Designing Tool Error Messages an Agent Can Actually Act On
A tool error is the next input to the model. Here is what Anthropic recommends for actionable errors, plus a small schema that makes failures decidable.
The Case for Synthetic Adversarial Personas in Your Eval Suite
Most eval sets are built from happy-path examples and yesterday's bugs. Neither one tells you how the system fails against a user trying to break it.
A Framework for Deciding Where an LLM Call Belongs in Your Request Path
Sync, streaming, or background job — the placement decision shapes your product more than the prompt does.
What to Look for in an LLM Observability Tool Before You Buy One
Pick LLM observability tooling by the failures you will actually have: trace completeness, per-step cost, stop reasons, version linkage, replay and privacy.
What to Cache — and What Never to Cache — in an LLM-Backed Feature
Caching feels like a free win for any expensive model call, but applied carelessly it turns a probabilistic system into a source of silent, confidently wrong answers.
Model Context Protocol (MCP): A Universal Adapter for AI Tool Integration
MCP is an open standard for connecting AI applications to tools and data. Here are the roles, the features, and the security rules in the current specification.
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