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Deep-dive guides on AI agents, agent orchestration, MCP, and developer tooling.

5 posts found

AI Agents

MCP vs. A2A: Why Two "Agent Protocols" Solve Completely Different Problems

MCP and A2A keep getting lumped together as competing agent protocols, but they standardize different layers entirely: one connects an agent to its own tools, the other connects independent agents across a trust boundary. Here's where that line actually falls, and when you need both, one, or neither.

October 26, 2026 · 10 min read
AI Agents

Durable Execution for AI Agents: How LangGraph, Temporal, Inngest, and DBOS Actually Survive a Crash

AI agent loops run long and cost real money per step, so a naive retry after a crash re-pays for work you already bought. Here is how four durable execution frameworks checkpoint that work, and exactly where each one's recovery model breaks if you trust the default settings.

October 20, 2026 · 12 min read
AI Agents

LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK: Four Different Bets on How Agents Should Talk to Each Other

Four multi-agent frameworks, four incompatible answers to the same question: when one agent hands work to another, what happens to the context? The choice determines what breaks in production.

September 9, 2026 · 8 min read
AI Agents

Pipeline, Supervisor, or Mesh: Where Each Multi-Agent Orchestration Pattern Actually Breaks

The three dominant multi-agent orchestration topologies each fail in a different, predictable way once you move past the demo. Here is how to pick one based on where your task actually breaks, not which pattern sounds more sophisticated.

July 4, 2026 · 8 min read
Evals & Benchmarks

Why Your Agent Benchmark Score Doesn't Predict Production Reliability

Benchmark leaderboards measure task completion under lab conditions, not the compounding step failures that sink agents in production. Here is the math, and a blueprint for an eval harness that actually predicts reliability.

July 1, 2026 · 7 min read

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