Agentic AI is best understood as a model embedded in a system that pursues a goal through repeated decisions, tool use, and feedback—not as a model that is automatically more capable or reliable. For engineers, five concepts clarify how these systems work and where their risks arise: the agent loop, tools and permissions, grounding and retrieval, context and runtime, and evaluation and safety. These are useful architectural lenses, not a single official five-part taxonomy.
What is an AI agent?
An AI agent is a model-powered system that directs its own process and use of tools toward a goal. Unlike a fixed script, it can choose a next step based on the current context, act, inspect the result, and adapt. Anthropic describes that distinction in Trustworthy agents in practice: “The practical difference between this and a chatbot is that an agent operates in a self-directed loop: it plans, acts, observes the result, adjusts, and repeats until the task is done or it needs to check in for human input.”
The label does not guarantee autonomy, competence, or safety. A system’s actual behavior depends on its model, instructions, connected tools, permissions, runtime, and controls. A workflow can also combine model-directed choices with deterministic code: the model may decide what to retrieve while application code enforces the sequence for a high-impact action.
1. The agent loop: planning, acting, and observing
The loop is the core distinction between an agent and a one-response interface. The model uses its current context to decide what to do next; the system executes that step, returns an observation, and the model decides whether to continue, change course, finish, or request help. Microsoft’s VS Code agent documentation describes a practical cycle of request, context and reasoning, tool action, validation, and review.
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- Interpret the goal. Identify the requested outcome, constraints, and information needed.
- Choose a step. Select an action, such as searching a source, reading a file, or calling an API.
- Act and observe. A connected tool runs and returns a result that becomes part of the agent’s context.
- Validate and continue. The agent assesses whether the result advances the goal, takes another step, or escalates to a person.
Planning can be adaptive: later actions depend on earlier results. That flexibility is useful for open-ended work, but every additional step is another opportunity for an error to affect what follows. For constrained tasks, engineers can keep critical transitions in fixed code rather than relying on the model to decide every control-flow detail.
2. Tools and permissions: the agent’s action boundary
A model alone cannot inspect a private database, send a message, or change a record. Tools connect it to information and actions. OpenAI’s practical guide to building agents groups tools into data, action, and orchestration categories. Data tools retrieve information; action tools change something outside the model; orchestration tools help delegate or coordinate work.
The set of connected tools and the permissions granted define what an agent can actually do. A read-only lookup is not equivalent to a payment, deletion, or message sent to a customer. Design permissions around the potential impact and reversibility of each action:
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- Grant only the access required for the task, and separate read access from write access where possible.
- Require confirmation or human approval before consequential or difficult-to-reverse actions.
- Make actions inspectable: record what was requested, which tool was called, and what result it returned.
- Use deterministic application checks for requirements that must always hold, such as authorization or transaction limits.
These controls should be applied at the tool and application boundary, not assumed to emerge from a well-worded prompt.
3. Grounding and retrieval: connect decisions to evidence
Grounding supplies information against which an answer or action can be checked. Retrieval-augmented generation (RAG) retrieves relevant material before generating a response. Google Cloud distinguishes this from GraphRAG, which uses explicit relationships among information, and agentic RAG, in which an agent plans multiple retrieval steps. Its core concepts of AI agents describe these as related but distinct approaches.
Use the simplest retrieval pattern that meets the task. A direct search can be enough when the answer is in one reliable source. Graph-based retrieval may help when relationships among entities matter. An agentic retrieval process can help with multi-hop questions that require finding one fact, using it to formulate another query, and combining the results.
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Agentic retrieval adds planning over retrieval infrastructure; it does not make ordinary search obsolete. More steps can increase latency and cost, and each retrieved result still needs appropriate provenance and validation. Retrieval also does not prove that an answer is correct: the system can miss relevant material or misinterpret what it finds.
4. Context, memory, and runtime: three different jobs
These terms are often blurred, but they describe different parts of an agent architecture:
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- Context is the information available to the model during a particular step, such as the user’s request, instructions, retrieved passages, and recent tool results.
- Memory and data architecture describe what information is retained, where it is stored, and how it can be retrieved later. An agent does not necessarily have persistent memory; a system may retain only what the application explicitly stores.
- Runtime is where execution and state management happen: it coordinates model calls, tools, and the flow of work.
These choices affect what the model can see, what survives between tasks, and who operates the execution environment. OpenAI’s Agents documentation describes differences in responsibility between a managed Agents API and an SDK run in the application. Engineers should decide deliberately which state belongs in the application, what the model receives as context, and which execution responsibilities are handled by a service or by their own runtime.
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5. Evaluation, oversight, and security
An agent should be evaluated as an end-to-end system, not just by whether a model produces a plausible answer. Tests should cover the goal, intermediate decisions, tool calls, returned results, and final outcome. Include ordinary cases as well as malformed input, missing data, tool failures, and attempts to steer the system outside its intended task.
Design controls for consequential actions
Set escalation points before deployment: define which actions require approval, which cases should stop for clarification, and how a person can inspect or reverse an action. OpenAI’s safety guidance for building agents describes prompt injection as malicious instructions embedded in untrusted text or data that may steer tool calls or expose private information. It recommends measures including structured outputs, clear guidance, guardrails, and approvals for MCP tool operations, while warning that agents can still make mistakes or be tricked.
Use layered safeguards rather than treating any one mitigation as a guarantee. Keep untrusted content distinct from trusted instructions, restrict tool permissions, validate outputs before sensitive actions, and make high-impact operations reviewable. A safeguard can lower risk; it cannot establish that every agent decision will be correct.
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Interpret safety disclosures carefully
The authors of the 2025 AI Agent Index, presented at FAccT ’26, reported that 25 of 30 agents in their sample disclosed no internal safety results and 23 of 30 had no third-party testing information. These are sample-specific counts about disclosure, not failure rates or a census of all deployed agents. The AI Agent Index paper offers a view of what was documented in that sample, not proof that systems without disclosed results are unsafe or safe.
How to choose an agent architecture
There is no single best balance between model control and fixed code flow. Compare designs against the job and its consequences, rather than choosing a framework because it is labeled agentic.
Quick Recap
| Decision area | Question to answer | Why it matters |
|---|---|---|
| Control flow | Which choices should the model make, and which steps must remain deterministic? | Adaptive planning handles variable tasks; fixed code can enforce critical transitions. |
| Tools and permissions | What can the agent read or change, and which actions need approval? | Capability and permission scope determine the possible impact of an error. |
| State and context | What information is available per step, and what must persist across tasks? | Context limits and persistence shape continuity, privacy, and data architecture. |
| Grounding | Which trusted data sources does the task require, and how should results be checked? | Retrieval patterns should match the information need and its relationships. |
| Runtime ownership | Which execution and state responsibilities belong to a managed service versus your application? | This affects deployment, integration, and operational control; OpenAI documents distinct runtime options in its Agents documentation. |
| Evaluation and oversight | How will quality, latency, cost, tool behavior, and escalation be tested? | A design must meet the task’s practical constraints as well as produce useful outputs. |
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