Agent
An LLM-driven system that can plan, call tools, observe results and iterate toward a goal, rather than producing a single one-shot response.
· Reviewed by senior engineers
An agent is an LLM-powered system that does more than answer a single question. It plans a sequence of steps, calls tools (APIs, databases, code interpreters, other agents), observes the results, and iterates until it reaches a goal or hits a stop condition. The model is the brain; tools are the hands; a control loop is the executive function.
The interesting use cases are tasks that need multiple steps and external systems: research assistants that browse and synthesise, customer service agents that look up orders and issue refunds, scheduling agents that negotiate calendars, code agents that write, run and fix code, sales assistants that qualify leads and update the CRM.
The failure modes are equally interesting. Agents can loop indefinitely, hallucinate tool outputs, call the wrong tool with confidence, get confused on long contexts, and quietly burn through budgets. The mitigations are bounded action spaces, strict tool schemas, retries with structured error handling, observability on every step, evals on real tasks, and circuit breakers that kill runs that go off the rails.
Devinsta builds agents for narrow, high-value tasks — not because agents can't do more, but because narrower scope yields more reliable outcomes for the same model. The general-purpose autonomous agent is still aspirational; the focused one is shipping value today.