AI Agents
An AI agent is a software system that can interpret a goal, decide what to do next, use available tools, and iterate toward an outcome.
That is more than simple text generation. A useful agent has a loop, access to external systems, and rules for when to continue, stop, or ask for help.
What Makes a System an Agent
Section titled “What Makes a System an Agent”A system starts to look agentic when it can do most of the following:
- accept a goal instead of only a single direct command
- decide between multiple next actions
- call tools such as APIs, databases, browsers, or code runtimes
- inspect results and revise its plan
- keep enough state to continue across multiple steps
The Basic Agent Loop
Section titled “The Basic Agent Loop”Most agent systems follow a variation of this cycle:
- Understand the goal.
- Decide the next step.
- Use a tool or generate an action.
- Observe the result.
- Continue, revise, or stop.
This is why pages about Agentic AI and the Spectrum of Agency belong next to this one. They describe the same idea at different levels of abstraction.
Core Building Blocks
Section titled “Core Building Blocks”The model handles reasoning, generation, classification, or planning prompts. It is the decision engine, but it is not the whole system.
Tools let the agent do something outside the model itself:
- search the web
- query a database
- send an email
- execute Python or shell code
- read or write files
Memory and state
Section titled “Memory and state”The agent needs enough context to avoid repeating itself and to keep track of what it has already done.
Examples:
- current conversation state
- retrieved documents
- intermediate results
- a task list or execution history
Policy and guardrails
Section titled “Policy and guardrails”Production agents need rules:
- what actions are allowed
- which tools require confirmation
- when the agent must stop
- what counts as failure or escalation
Common Agent Patterns
Section titled “Common Agent Patterns”| Pattern | What it is good for |
|---|---|
| Tool-using assistant | Focused workflows with API calls or retrieval |
| Planner-worker | Breaking a task into substeps and delegating work |
| Research agent | Search, retrieval, synthesis, and citation |
| Coding agent | Code changes, testing, debugging, and repo workflows |
| Multi-agent team | Separating planner, executor, reviewer, or critic roles |
When To Use an Agent
Section titled “When To Use an Agent”Use an agent when the problem requires:
- multiple decisions in sequence
- tool use
- branching logic
- recovery from partial failure
- coordination across several steps
Do not default to an agent for tasks that are really single-shot generation problems. If a direct prompt plus retrieval works, that is usually simpler and more reliable.
Production Concerns
Section titled “Production Concerns”Agents fail in ways standard apps do not.
Watch for:
- hallucinated tool assumptions
- repeated loops
- brittle prompts
- hidden state drift
- poor cost control
- weak observability
Useful production disciplines include:
- execution tracing
- step-level logging
- replayable test cases
- human approval gates for high-risk actions
Practical Reading Path
Section titled “Practical Reading Path”- Read Agentic AI for stronger autonomy patterns.
- Read Spectrum of Agency to classify the level of autonomy you actually need.
- Read API Tokens and Access Guides if your agents will call external services.
- Use Python when you are ready to implement agent workflows.