An AI agent is a software system that pursues a goal by interpreting inputs, choosing actions and, when needed, using tools to affect or gather information from its environment. Unlike a model that only returns an answer, an agentic system can act, inspect the results and decide what to do next. Its autonomy is bounded by the goals, rules, permissions and stopping conditions set by people.
What “agent” means in AI
There is no single universally settled definition of an AI agent. Some definitions emphasize learning, persistence or broad adaptation; others include systems that take bounded independent actions. The OECD’s 2026 synthesis describes agents as systems that perceive and act on their environment with some autonomy, use tools as needed to achieve goals and adapt to changing inputs and contexts.
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“Agentic AI” generally refers to systems or approaches that let agents make decisions and take actions toward goals. It does not mean that every agent is a fully independent or general intelligence. People define the task, choose which tools and data the system can access, set its rules and determine when it must stop or seek approval.
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A generative model can produce text or other content in response to a prompt. An agentic system can use a model as part of a control loop: it selects an action, calls a tool, receives the result and continues, revises its plan or stops. The categories overlap—many current agents use a large language model to interpret a task and choose what to do.
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How an AI agent works
A typical agent workflow moves between a goal, an action and feedback:
- Receive a goal and constraints. A user or another system specifies what outcome is wanted and any relevant limits.
- Interpret and plan. The agent works out what the task requires and may divide it into smaller steps.
- Choose an action. It selects from available tools or actions, such as querying a database, calling an API, searching the web or running a software function.
- Observe the result. It uses the tool’s response as feedback. It may take another step, revise its approach, ask for help or stop.
- Return an outcome. It provides a result and, where the system supports it, an activity record that people can review.
Not every agent has long-term memory. One system may rely only on the current task context; another may retain selected facts or workflow state across tasks. Google Cloud describes working conversational context separately from longer-term grounding and memory, alongside concepts such as models, tools, data, orchestration and runtime.
What an agent is made of
These are useful concepts for understanding agent systems, not a mandatory parts list. Product terminology varies, and a simple agent may combine several functions.
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- Goal and rules: Specify the intended outcome and boundaries, including what the system may or may not do.
- Tools: APIs, databases, web search, software functions or other capabilities that let the agent obtain information or act on another system.
- Grounding and data: Supply task-specific or current information. What data is available—and under what access limits—can affect the result.
- Memory and state: Keep track of the current task and, in some designs, selected facts or workflow progress between tasks.
- Planning and orchestration: Determine what to do next, divide work into steps or coordinate multiple agents.
- Runtime, permissions and observability: Provide the execution environment, access controls and ways to handle failures and inspect activity, such as traces or metrics.
Single-agent and multi-agent systems
A single agent is a sensible starting point for a contained task: it keeps the core logic and tool definitions together. Multiple agents can divide a more complex objective among specialized roles, but coordination introduces its own trade-offs.
| Pattern | How it works | Useful when | Trade-offs |
|---|---|---|---|
| Single agent | One agent uses its instructions and available tools to complete a request. | The task is contained and one agent can handle its steps and context. | As tool count and task complexity grow, tool-selection errors, latency or incomplete tasks may become more likely. |
| Multi-agent | Specialized agents divide work and an orchestration process coordinates their contributions. | A task benefits from distinct roles or specialized context. | Coordination adds complexity, and can increase concerns around permissions, evaluation, reliability and computational cost. |
Google Cloud’s agent design guidance recommends starting with a single-agent pattern while refining its logic and tools, then considering whether task demands justify adding agents. Compare the options against task complexity, latency, cost, permissions, specialized context, failure recovery and the need for human review. For predictable work that fits a single model call or fixed workflow, agentic infrastructure may be unnecessary.
Examples of what agents can do
The examples below illustrate patterns, not a guarantee that an agent will perform reliably in every deployment.
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- Customer support: An agent can query an order database to retrieve an order’s status.
- Research assistance: An agent can call APIs to gather information and summarize it.
- Document workflows: IBM describes an insurance-client implementation built with Dynamiq that routes routine legal queries to a lower-cost classifier and complex cases to a research agent. IBM reports that contract-review time fell from 90 to 45 minutes in that case; this is a vendor-published example, not evidence of a general productivity result.
Research, support, knowledge work, software and workflow automation may suit agentic approaches when a task is open-ended enough to benefit from tool selection and multiple actions. For simple summarization, translation or classification, a direct model call may be cheaper and easier to control.
What can go wrong, and how to limit the risks
An agent may choose the wrong tool, repeat calls in a feedback loop, fail to complete a complex task or add latency and cost. Integrations can expose data, while failures in a multi-agent workflow may affect dependent steps. Google Cloud’s guidance emphasizes secure runtime execution, identity, policies, network access, error handling, monitoring and execution traces.
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Practical safeguards include:
- Give the agent only the permissions required for its task, and restrict its accessible data and available actions.
- Set maximum iterations and clear stopping conditions so it cannot continue indefinitely.
- Log actions and tool results, and monitor activity for failures or repeated calls.
- Evaluate whether tasks are completed and examine failure modes, rather than relying only on plausible-sounding responses.
- Provide a way for a person to interrupt the system or approve consequential actions.
- Test for prompt injection and data-exfiltration scenarios in the tools and data sources the agent can access.
IBM discusses activity logs and real-time monitoring for loop risks; Google Cloud highlights execution security, access controls, reliability and observability. These are prudent design practices, not guarantees that an agent is safe or correct.
What public safety information tells you—and what it doesn’t
The MIT AI Agent Index research team’s The 2025 AI Agent Index, published for FAccT 2026, found disclosure gaps in its reviewed sample:
- Of 240 reviewed safety, evaluation and social-impact fields, 135 had no public information.
- Of 30 reviewed agents, 25 disclosed no internal safety results and 23 had no third-party testing information.
- Sandboxing or virtual-machine isolation was documented for 9 of the 30 agents.
- Prompt-injection vulnerabilities were documented for 2 of 5 browser agents.
These counts describe the Index’s selected sample and publicly available disclosures, not every deployed AI agent. Missing public information is not proof that a system is unsafe, just as a disclosed control is not proof that a system cannot fail. For a system you plan to use, ask what was tested, what access it has and which safeguards are documented.
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NIST, as quoted in the OECD’s 2026 report, defines AI agent systems as having “the capability for autonomous decision-making and taking action to operate with limited human supervision to achieve complex goals.” That emphasis on acting toward a goal captures both the appeal and the extra responsibility: an agent can do more than generate a response because it can interact with tools, but those actions need boundaries and oversight.
Use an agent when a task needs a sequence of decisions and tool interactions, and when the value of that flexibility justifies the added work of permissions, monitoring and evaluation. Start with one agent for a contained workflow; add specialized agents only when the task warrants the coordination overhead. If a fixed process or a single model call is enough, the simpler option is often easier to manage.
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