Direct answer
The answer in brief
An agent combines a model with tools, state, control logic, and evaluation.
Review the 2 cited sources →Key takeaways
- An agent combines a model with tools, state, control logic, and evaluation.
- Autonomy should be limited by risk, reversibility, and clear approval boundaries.
- A deterministic workflow is often better when the process and decisions are already known.
Field tool
A decision rule you can apply now
Use an agent only when the sequence must adapt to intermediate evidence and the actions can be bounded, observed, and stopped.
Compare an assistant, a deterministic workflow, and a bounded agent on the same low-risk task using identical acceptance criteria.
The proposed agent adds autonomy but no measurable benefit over a simpler workflow, while increasing permissions and failure modes.
01
The minimum definition
An AI agent receives a goal, observes available context, selects an action, uses a tool or produces an output, and evaluates what should happen next. The language model may propose decisions, but the surrounding application controls which tools exist, what data is available, and which actions are permitted.
This distinction matters because a model alone cannot reliably guarantee permissions, persistence, or business rules. Production behavior comes from the complete system: model, instructions, tool interfaces, state store, orchestration, policies, and monitoring.
02
Core components of an agent
A useful architecture gives each component a narrow responsibility. The model interprets ambiguous information. Tool adapters expose safe operations. State records progress. The orchestrator decides when to continue, retry, escalate, or stop.
- Goal and success criteria
- System instructions and operating policy
- Tools with typed inputs, permissions, and error responses
- Short-term working context and durable state
- A control loop with budgets and stop conditions
- Tracing, evaluation, and human approval
03
Agent, assistant, or workflow?
Use a normal assistant when a person remains in control of every request. Use a deterministic workflow when the steps are known and exceptions are limited. Use an agent only when the system must choose among tools or adapt the sequence based on intermediate evidence.
The most dependable production systems are often hybrid. Rules handle identity, routing, calculations, and approvals; AI handles classification, extraction, synthesis, or drafting inside controlled boundaries.
04
Design autonomy around risk
Autonomy is not a single switch. A research agent can be allowed to search and summarize while being prohibited from publishing. A support agent can draft a response while requiring approval before sending. A purchasing workflow can recommend an action but never execute a payment.
For every tool, define who may call it, what inputs are allowed, which actions are reversible, and when a person must approve. Add time, cost, and tool-call limits so failure cannot become an endless loop.
05
Evaluate the complete trajectory
Final-answer quality is only one metric. Review whether the agent chose the right tool, used valid arguments, respected permissions, handled errors, cited evidence, stopped at the right time, and kept cost and latency within budget.
Build a representative test set from real tasks and difficult edge cases. Re-run it whenever the model, prompt, tools, retrieval layer, or business policy changes.
Editorial review
How this guide was produced
Reviewed by Yassine Sebaoui on August 8, 2026. A vendor-neutral architecture explainer for distinguishing assistants, deterministic workflows, and bounded agents.
Review method
- Decomposed agent systems into model, instructions, tools, state, orchestration, permissions, and evaluation.
- Used reversibility and decision ambiguity as the main criteria for choosing autonomy.
- Reviewed the examples for clear ownership and human escalation.
Limits
- The term AI agent is used inconsistently across products and research.
- The article explains architecture; it does not rank platforms or certify production readiness.
Read our complete editorial methodology or report a correction.
FAQ
Common questions
Does an AI agent need long-term memory?
No. Many agents need only task state. Durable memory should be added only when a defined use case requires it, with retention, privacy, correction, and deletion rules.
Are multi-agent systems always better?
No. Multiple agents add coordination cost and new failure modes. Start with one controlled workflow and add specialized agents only when the separation can be measured and tested.
Sources
Primary sources and live documentation
These links point to authoritative documentation used to verify and maintain this guide for the August 8, 2026 update.
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