Agent Authentication
The process of verifying an agent's identity and authorizing its access to tools, data, and services. Agent authentication ensures that only approved agents can perform sensitive actions and that actions are traceable to specific agents.
Agent authentication is increasingly important as agents interact with production systems on behalf of users or organizations. Unlike human users who authenticate interactively, agents need programmatic authentication mechanisms: API keys, OAuth tokens, service accounts, or certificate-based auth. Each agent should have its own identity with the minimum permissions required for its tasks.
For security-conscious teams, implement agent authentication at multiple levels. Tool-level auth ensures each external service call includes valid credentials scoped to the agent's role. User-level delegation allows agents to act on behalf of specific users with their permissions. Audit-level tracing logs which agent performed which action with which credentials. Avoid sharing credentials across agents, as this makes it impossible to audit or revoke access granularly. Rotate credentials regularly and monitor for unusual access patterns. As agent systems scale, consider implementing an agent identity provider that manages credentials centrally and enforces policies like least-privilege access and time-limited tokens.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.