An agent can be understood as a controlled loop around a model: choose an action, use a tool, inspect the result, update state, and decide whether to continue.
Decision point
The system chooses whether to reason, retrieve, call a tool, or finish. Available actions should match capabilities the application can actually enforce.
Tool contract
Every tool needs an input schema, output shape, timeout, and failure behavior. The agent is only as reliable as the interfaces it calls.
Observation and state
Tool outputs become observations; memory decides which information should remain in active context or persist across turns.
Stop conditions
Maximum steps, timeouts, invalid-action handling, and a finalization rule prevent open-ended loops from becoming incidents.
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AI Developer / ML Engineer building end-to-end AI systems from research to production, with a focus on multimodal AI, LLM applications, retrieval, MLOps, and systems engineering. He is based in Rawalpindi, Pakistan and is the founder of GROVE SYSTEMS.