Moving Beyond LLM Wrappers to Autonomous Agents

Most 'AI products' are a prompt and a text box. Autonomous agents need state, tools, evaluation loops and a failure budget.

A wrapper turns a user message into a completion. An agent owns an objective, decomposes it, calls tools, verifies its own output and escalates when confidence drops. The distance between the two is engineering, not prompting.

Start with the control loop. Define the agent's state machine explicitly: plan, act, observe, critique. Persist every step so runs are replayable — an agent you cannot replay is an agent you cannot debug.

Then treat tools as production APIs. Typed schemas, idempotency keys, timeouts and permission scoping. Most agent failures in production are not reasoning failures; they are integration failures with no retry semantics.

Finally, build the evaluation harness before scaling usage. A golden set of 50 real tasks with deterministic scoring will tell you more than any benchmark, and it converts model upgrades from a gamble into a measurement.