What is AI Agent?
Agentic AIA software system built around a language model that runs a loop: perceive the environment, reason about options, plan the next steps, act through external tools, and learn from the outcome. The model does the reasoning; the agent's code does the acting.
Why It Matters
An AI agent is not a model with a chat box in front of it. The model produces text. The agent is the machinery around it that turns that text into action: tools the code can call, memory of previous steps, instructions on what it may and may not do, and the loop that carries results back into context. The distinction matters because the failure modes live in the machinery, not the model.
The Five Components
Perception. Gathers what the environment looks like: API responses, retrieved documents, tool results, user messages.
Reasoning. The LLM’s job. Interprets what perception gathered and weighs the options.
Planning. Breaks the goal into ordered steps the available tools can execute.
Action. Executes each step through tool calls. The model never runs anything itself; it outputs a structured request and the agent’s code executes it.
Learning. Feeds the outcome back so the next decision is informed by the last one.
The loop repeats until the goal completes or the agent hands control back to a human. Each step’s result goes back into the model’s context for the next decision, and everything around that exchange is engineering.
Where It Breaks
The loop has no natural stopping point. An agent that cannot satisfy its goal keeps trying: one runaway customer-service conversation ran to $380 because nothing bounded the retries. Each component is also its own failure surface. Perception can be poisoned, tools rot underneath a running system, and memory carries stale context into new runs.
How Flytebit Handles It
Agent builds at Flytebit start from the loop, not the model choice: boundaries, tool contracts, and observability are part of the first architecture review. That is why our technical guide to how agents work spends half its length on the governance layer, and why how we build agentic systems starts with the operating envelope before any code.