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6 posts on Agents
A 10-step AI agent pipeline at 90% per-step reliability succeeds only 35% of the time. This is the compounding reliability math that explains why 78% of companies run pilots but only 14% ship agents to production — and the architecture that closes the gap.
Long-running agents fail 90% more often without state persistence. This is the memory architecture — working, episodic, semantic, procedural — that makes stateful AI production-ready.
Anthropic's 2026 Agentic Coding Trends Report shows devs use AI in 60% of their work but fully delegate only 0–20% of tasks. Here's the exact playbook to close that gap with Claude Code Agent Teams.
Ten months after MCP went multi-vendor, most teams are still treating it as a nicer function-calling wrapper. That's the wrong mental model — and it's quietly producing architectures that don't scale.
A MAST taxonomy of 1,600+ execution traces maps 14 failure modes across 3 root causes. The model is almost never the problem. The orchestration architecture almost always is.
82% of AI teams say prompt engineering alone isn't enough. The ones succeeding in production are treating context design the same way they treat database indexes — as an architectural decision, not a prompt trick.