An AI agent works by combining a model with instructions, tools, memory, and a control loop that repeatedly decides what to do next. The model supplies judgment and language capabilities, while the surrounding software turns those capabilities into a stateful process that can act, inspect results, recover from errors, and stop.
Understanding this architecture is more useful than treating an agent as a single intelligent object. Most successes and failures arise from how the components interact: an excellent model can be undermined by vague tools, stale memory, excessive permissions, or a control loop with no reliable definition of completion.
The Five Core Parts of an AI Agent
1. the Model
The model interprets the objective, reasons over the available context, and selects an action. In many current agents, this is a large language model capable of following instructions and producing structured tool calls as well as natural language.
The most capable model is not automatically the best choice for every step. A system may route difficult planning to a stronger model, use a faster model for classification, and rely on deterministic code for validation. This mixture can improve speed, cost, and reliability.
2. Instructions
Instructions define the agent’s role, boundaries, priorities, and output requirements. They can include a system prompt, task-specific context, policies, examples, tool descriptions, and stopping criteria.
Good instructions are operational. They tell the agent what evidence is required, when to ask for approval, which sources are acceptable, and how to recognize completion. Rules that are vague or contradictory force the model to guess, creating inconsistency across otherwise similar tasks.
3. Tools
Tools connect the model to capabilities outside its current context. A tool might search the web, retrieve a customer record, run code, query a database, control a browser, or create a calendar event.
The model normally does not execute the function itself. It chooses a named tool and proposes structured arguments. The agent runtime validates that request, checks permissions, executes the operation, and returns the result. This separation is essential: it gives software a chance to reject malformed or unsafe actions before they affect the outside world.
4. State and Memory
State is the information the agent needs during the current run: the objective, conversation, plan, observations, tool outputs, and completed steps. Memory extends that concept by retaining useful information beyond the immediate context, such as prior preferences, recurring facts, or lessons from earlier tasks.
More memory is not always better. Irrelevant records consume context and can steer the model toward outdated assumptions. Effective memory systems decide what to store, how to organize it, when to retrieve it, and how to handle conflicting or expired information.
5. the Control Loop
The control loop is the orchestration layer that keeps the process moving. It sends the current state to the model, receives a proposed action, runs approved tools, records the observation, and invokes the model again.
Anthropic describes an agent as an augmented language model operating in a loop with capabilities such as retrieval, tools, and memory in its guide to building effective agents. OpenAI similarly frames agent execution as an ongoing interaction among the model, its tools, and an environment in From Model to Agent.
The Interfaces Matter as Much as the Components
An architecture diagram can make each component look cleanly separated, but real reliability depends on the contracts between them. The model needs tool descriptions that distinguish similar capabilities. The runtime needs typed arguments and explicit error states. Memory retrieval needs provenance and freshness information. The completion checker needs criteria that can be tested rather than a vague feeling that the answer is good enough.
Consider a search tool that returns an empty list. That result could mean no relevant records exist, the query was malformed, the user lacks permission, or the service timed out. If the tool collapses all four conditions into the same output, the model cannot reason reliably about what happened. A well-designed interface returns structured evidence: status, source, timestamp, query, result count, and a machine-readable error when appropriate.
The same principle applies to context. Instructions, authoritative records, retrieved passages, model-created notes, and untrusted external content should not be treated as equivalent text. Labeling their source and authority helps the runtime enforce policy and helps the model weigh evidence correctly. This is a practical form of context engineering: deciding not only what information the model sees, but how that information is organized and what the system allows it to control.
A Step-by-Step Example
Imagine an agent asked to compare three potential suppliers and prepare a recommendation.
Agent runtime
Routes decisions
Maintains state
Model alone
Predicts tokens
Cannot execute
| Model | Interprets context and proposes the next action. |
|---|---|
| Runtime | Validates calls, executes tools, and returns observations. |
| Memory | Carries selected state between steps or sessions. |
| Control loop | Decides whether to continue, retry, escalate, or stop. |
- Receive the goal: the agent reads the decision criteria, deadline, budget, and required output.
- Inspect the available context: it checks whether the supplier names, internal requirements, and source documents are present.
- Form a plan: it decides to gather pricing, security information, service terms, and customer evidence for each supplier.
- Select a tool: it searches an approved document store or calls an external research tool.
- Observe: the runtime returns results, including possible errors or missing fields.
- Update state: the agent records what it learned and marks unresolved questions.
- Adapt: it changes queries, consults another source, or asks a person for an unavailable document.
- Verify: it checks that every recommendation is supported and that comparisons use the same criteria.
- Stop or request approval: it produces a draft recommendation, but leaves a purchasing decision to the authorized person.
The important point is that the sequence was not fully hard-coded. The system selected steps in response to what it found, but it still operated inside designed limits.
Planning Is Not Always a Separate Phase
Some agents produce a full plan before acting. Others decide one step at a time. Many use a hybrid: create a rough plan, execute the next action, and revise the remaining plan as observations arrive.
Long, rigid plans can become obsolete after the first unexpected result. Purely reactive agents can wander or repeat work. A practical design keeps enough planning to maintain direction while allowing replanning when the environment changes.
The ReAct framework is a foundational example of interleaving reasoning with actions and observations. Its central insight is that an external result can correct, refine, or redirect the next reasoning step.
How Agents Know When to Stop
Stopping is a system design problem. A model may declare success too early, continue polishing after the objective is met, or loop when a tool repeatedly fails.
Reliable agents combine several stopping mechanisms:
- Completion criteria: explicit conditions such as required fields, passed tests, or verified citations.
- Budgets: limits on steps, time, model tokens, tool calls, or cost.
- Error thresholds: escalation after repeated failures or low-confidence observations.
- Approval gates: a pause before high-impact or irreversible actions.
- External graders: deterministic checks or separate models that judge whether the output satisfies the task.
Common Agent Architectures
A single-agent loop is the simplest design: one model repeatedly uses tools until it finishes. It is easier to debug and often sufficient.
A router classifies the request and sends it to a specialized prompt, tool set, or model. Routing reduces irrelevant choices and can apply different policies to different work.
An orchestrator-worker architecture lets a lead agent create subtasks and delegate them to workers, then synthesize their results. This is useful when work can run in parallel or requires different specialties, but it increases token use and coordination failure modes.
An evaluator-optimizer loop separates generation from critique. One component produces an answer; another checks it against defined criteria; the first revises it. This works well when quality is measurable and improvement through iteration is worth the additional cost.
Failure to prevent: Confusing the model with the full runtime hides the software that grants authority and carries state.
What Usually Goes Wrong
- Poor tool descriptions: the model chooses the wrong capability or supplies invalid arguments.
- Unbounded context: long transcripts fill with irrelevant detail and bury decisive information.
- Silent tool errors: an empty or partial result is mistaken for a valid observation.
- Weak grounding: the agent acts on an assumption instead of checking the system of record.
- Excessive autonomy: the agent can take consequential actions without an appropriate review boundary.
- No trajectory evaluation: teams judge the final answer but do not inspect how the agent reached it.
Design Principles for Dependable Agents
Start with the smallest architecture that can solve the task. A deterministic workflow should handle known steps; reserve model discretion for decisions that genuinely require interpretation. Give each tool a narrow purpose, typed inputs, explicit error states, and least-privilege access.
Make state visible. Log every tool call, result, retry, approval, and model decision needed for diagnosis. Compress old context instead of endlessly appending it, and preserve authoritative data separately from model-generated summaries.
Design the runtime so that failures are explicit. A tool should distinguish “no records found” from “request failed,” and the state store should distinguish verified facts from model-generated summaries. Otherwise, the model may treat an absence caused by a timeout as evidence that something does not exist.
Finally, evaluate the complete system. Run the same task multiple times, measure success and resource use, and inspect trajectories for policy violations or fragile shortcuts. Anthropic’s guide to agent evaluations stresses that agents need tasks, repeatable trials, transcripts, and graders—not a handful of impressive demos.
What to Remember About How AI Agents Work
An AI agent is an engineered loop, not just a smart model. The model decides; tools act; memory carries state; the environment returns evidence; and the control loop determines what happens next.
When those parts have clear interfaces and boundaries, an agent can handle open-ended work that conventional automation cannot anticipate. When they do not, autonomy amplifies ambiguity. The quality of an agent therefore depends as much on system design, permissions, and evaluation as it does on the underlying model.
