Agentic AI Architecture

LLMs call external functions mid-inference to reason and act in loops.

Prompt injection attacks succeed more often against agents than containers can stop them.

Agents accumulate sensitive data as a side effect of their work, and nobody knows how to control it.

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.

Confusion over naming conventions delays multi-agent architecture decisions before code is written.

Standard auth protocols weren't built for agents spawning agents at machine speed.

Agents handle complexity and variation where traditional automation breaks down.