AI agents typically operate in a loop: understand the goal, collect context, plan a next step, use a tool, observe the result, update state and decide whether to continue.
1. Goal and context
The agent begins with a user request, system objective or event. It combines that goal with available context: conversation history, files, retrieved documents, application state or structured business data.
Good context engineering matters because an agent can only reason from what it can see. Too little context produces blind spots; too much irrelevant context can reduce reliability and increase cost.
2. Planning the next step
The model may create an explicit plan or choose one action at a time. Some systems use a planner-executor pattern; others rely on iterative tool calls without a visible plan.
Planning should remain bounded. A production agent usually benefits from clear stopping conditions, maximum step counts and rules that define when it must ask a human rather than continue on its own.
3. Tool use
Tools are how an agent leaves the chat interface. A tool can search the web, read a database, execute code, create a ticket, send a message or call another service.
The tool layer should validate inputs and enforce permissions. The model should not be treated as the access-control system. Credentials, allowlists, schemas and approval rules belong in the surrounding software.
4. Observe and adapt
After a tool runs, the agent receives a result and decides what it means. It may need to retry, choose another tool, ask for clarification or revise the plan.
This feedback loop is what makes agents flexible, but it also creates new failure modes. A bad result can lead to another bad action unless the system validates outputs and limits compounding errors.
5. Memory and state
Working state helps the agent track progress during a run. Longer-term memory can preserve user preferences, facts or task state across sessions. Retrieval systems can provide external knowledge without turning every retrieved item into permanent memory.
Memory needs retention, ownership and deletion rules. A remembered instruction can influence future actions, so memory should be treated as data with security implications.
6. Control and observability
Identity, permissions and human approval determine which actions are actually possible. Tracing and logs show what the agent attempted, which tool ran, what input it received and what output was returned.
Without observability, debugging and governance become difficult. A production agent should make it possible to reconstruct important actions and distinguish model reasoning from tool execution and external state changes.
Failure and recovery
Agents can fail because of missing context, tool errors, ambiguous instructions, prompt injection or incorrect assumptions. Robust systems handle these failures explicitly through retries, fallbacks, escalation and rollback rather than simply continuing indefinitely.
Evaluations should include realistic failure cases, not only successful demonstrations. The operational quality of an agent is defined by how it behaves when the world does not match its expectations.
Frequently asked questions
Does an AI agent always create a plan?
No. Some agents plan explicitly; others choose the next action iteratively.
What makes tool use safe?
Permissions, input validation, scoped credentials, approvals and logging around the tool call—not the model alone.
Why are logs important?
They make actions traceable and help teams understand failures, policy violations and unexpected tool behavior.