AI Agents — Page 2

Five architecture patterns that determine which AI agent projects ship and which get cancelled.

Enterprises are choosing multi-agent patterns before standards settle, locking in costly tradeoffs.

Real agents think and adapt; most "AI agents" are just fixed prompt chains dressed up in marketing.

Most enterprises have deployed AI agents but lack basic visibility into what they've built.

Human oversight gaps create dangerous new attack surfaces as AI agents operate autonomously.

Structured spans across tool calls reveal where agents actually fail, not just that they failed.

LLMs return confident wrong answers that look like success to traditional monitoring tools.

How autonomous agents drift toward policy violations despite passing individual safety checks.

Agent baselines must be per-agent and per-context, not cross-company averages.

Organizations miss most shadow AI because detection methods only catch pieces of the problem.

Agents emit semantic failures that infrastructure metrics miss, requiring new telemetry pipelines.