AI agents are systems that use LLMs to reason about tasks and take actions autonomously. Unlike simple chatbots, agents can use tools, access external systems, and complete multi-step workflows.
Agent Components
- LLM: The reasoning engine
- Tools: APIs, databases, code execution
- Memory: Context and history
- Planning: Task decomposition
Agent Safety Risks
- Excessive agency—taking unintended actions
- Tool misuse—incorrect API calls
- Prompt injection through tools
- Runaway loops and costs
How do I make agents safe?
Limit permissions, require human approval for sensitive actions, monitor all tool calls, and implement guardrails on outputs.
Practical example
An assistant searches a policy, decides it needs an order lookup, and uses an authorized tool before answering.
What to check
Trace each decision, tool attempt, termination condition, and final application state.
Limits and tradeoffs
Giving a model tools does not make it reliable or authorize every action it proposes.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.