"An agent is not just a model that responds—it's a system that perceives, plans, acts, and learns from outcomes in pursuit of a goal."
An agentic system operates with autonomy, goal-orientation, and adaptive behavior. It doesn't just execute predefined scripts—it reasons about what to do next.
True agents perceive their environment, maintain internal state, plan action sequences, execute using available tools, and learn from feedback. They operate in loops, not in one-shot transactions.
Agents decompose high-level objectives into executable sub-goals, track progress, and re-plan when obstacles arise.
Agents reason about action sequences, anticipate outcomes, and construct multi-step plans to achieve goals efficiently.
Agents select and invoke tools (APIs, databases, calculators) to extend their capabilities beyond pure language understanding.
Agents observe the results of their actions, learn from failures, and adjust behavior through iterative refinement.
Agents maintain episodic memory of past interactions and semantic knowledge to inform future decisions.
Agents operate within defined boundaries, respect permissions, and have guardrails to prevent harmful actions.
This loop repeats until the goal is achieved or constraints are met.
Agent alternates between reasoning steps (thinking aloud) and action steps (tool invocations), creating an interpretable trace of thought and behavior.
Agent breaks down complex problems into reasoning chains, invoking tools at strategic points to gather evidence or perform calculations.
Agent decomposes high-level goals into sub-tasks, delegates to specialized sub-agents, and synthesizes results for final output.
Agent generates an initial output, critiques it against criteria, and iteratively refines until quality thresholds are met.
Agents can fail, hallucinate, or get stuck in loops. Design with timeouts, retries, and human-in-the-loop overrides.
Multi-step reasoning consumes tokens rapidly. Set budgets, optimize context, and monitor per-task costs.
Autonomous agents can take unintended actions. Enforce permissions, validate outputs, and implement guardrails.
Multi-step agent traces are complex. Instrument every decision, log tool calls, and visualize agent reasoning.