Agent Observability: How to Trace, Evaluate, and Debug AI Agents in Production
Classic LLM observability breaks for agents. The telemetry model, four failure classes, trajectory evaluation, and a build order a platform team can ship.
Classic LLM observability breaks for agents. The telemetry model, four failure classes, trajectory evaluation, and a build order a platform team can ship.
Why API keys break for AI agents, the delegation patterns replacing them, how to scope authorization, and the audit trail attribution requires.
Agent interop standards are not neutral plumbing. Whoever owns the protocol layer between agents and enterprise systems owns the distribution chokepoint.
Consumer agentic commerce is stalled on liability and mandate problems. B2B receivables is not, and that is where agent-initiated payments are shipping now.
Agents rarely shrink payrolls. They convert doing-work into checking-work, and winning org charts are redesigned around that conversion, not headcount.
AI agents break the per-seat model at the root. The pricing ladder replacing it, the failure modes of each rung, and the buyer playbook for the transition.
Prompting is a commoditized layer now. The real leverage moved to context engineering: assembling the right information into the window at the right time.
Most enterprise AI agents stall in pilot. A framework for the narrow, tool-constrained, well-evaluated patterns that ship, and the demoware that does not.
AI agent frameworks demo well and break in production. Three structural failure modes, a comparison of LangGraph, CrewAI, AutoGen, and what to use instead.
Deep analysis across the systems, strategies, and economics that shape modern technology.
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