The stateless model problem
Your favorite LLM is stateless. It takes text in, predicts the next tokens, and stops. It has no memory of what it did five seconds ago, and it certainly can't click a button, query a database, or fix a typo on its own. To do that, it needs a Harness.
In the AI engineering world, there's a simple formula that's becoming the standard:
Agent = Model + HarnessThe model is the cognitive core, but the harness is the nervous system and the muscles. It is the code that drives the model through its execution loop, parses its tool-calling intents, executes those tools, manages short- and long-term memory, and decides when to safely stop.
What goes inside a robust harness?
- The Execution Loop: The continuous Cycle of Observation -> Decision -> Action -> Observation.
- Context Engineering: Actively curating what goes into the LLM's limited context window at each turn.
- Deterministic Guardrails: Hard-coded safety boundaries, budget caps, and rate limits.
- Decision Traces: Comprehensive logs of every step, prompt, and tool call for debugging.
Without a robust harness, your AI agent is just a expensive chatbot waiting to get stuck in an infinite loop.