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To build an AI agent, define one specific job, give a model instructions and only the tools it needs, decide what information should persist between turns, then add testing, tracing, permission controls, and recovery paths. Start with one focused agent; add specialist agents or handoffs only when separate responsibilities or access rules justify the extra coordination.
What is an AI agent, and what do you need to build one?
An AI agent is more than a prompt sent to a model. It combines a model, instructions, tools, and runtime behavior: the application or service that manages context, calls tools, handles results, and decides how work proceeds. OpenAI’s agent-building guidance describes a focused task and defined tools as a starting point; a prompt alone does not provide the execution and control needed for a production system.
Before choosing a framework, write down the outcome the user needs and the boundaries around it. For example, an illustrative support-triage agent might classify an incoming request and draft a reply, but not send that reply or change an account without approval. That distinction turns a vague goal—“help support”—into a task with observable completion and explicit limits.
A useful first version has four parts:
- Instructions: What the agent is responsible for, what it must not do, and what a complete answer looks like.
- Tools: The specific application functions or services it can invoke, with permissions narrow enough for the task.
- State: The information needed for the current turn, plus any carefully selected information that must survive later turns.
- Runtime controls: The code or managed service that executes tools, handles errors, applies approvals, and records what happened.
These parts are interdependent. A tool is not just a convenience: its definition and permission scope form part of the agent’s interface and security boundary. Likewise, memory is not simply “more context”; it requires decisions about what to retain, retrieve, and update.
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Which runtime should you use?
Choose a runtime based on who should control the loop, state, tool execution, and recovery—not on the assumption that one option is universally best. OpenAI’s runtime comparison describes three choices: an application-owned Responses API loop, SDK-managed agent workflows, and the managed Agents API.
| Runtime | Who owns orchestration? | Best fit | What to consider |
|---|---|---|---|
| Responses API | Your application owns the control loop. | Use it when you need to manage history, tool handling, and the execution environment directly. | You have the most direct responsibility for implementing and maintaining the loop and its recovery behavior. |
| Agents SDK | The application defines agents and uses SDK-managed turns and workflows. | Use it when reusable agent definitions, tools, handoffs, guardrails, and structured outputs are useful. | Determine which workflow behavior the SDK manages and which application policies and integrations you still own. |
| Agents API | OpenAI provides managed orchestration for durable sessions. | Use it when managed sessions, context compaction, recovery, tools, MCP connections, or optional sandboxes match the application. | Check that the managed deployment and recovery model fit your requirements for control and operations. |
These distinctions are architectural, not performance rankings. The available documentation does not establish a universal cost, speed, or benchmark winner. OpenAI announced the Responses API, built-in web search, file search, computer use, the Agents SDK, and integrated observability on March 11, 2025, presenting them as building blocks for agentic applications. That announcement is useful context for the platform’s design, not a guarantee that every capability fits every deployment.
How should you decide?
- Choose the Responses API if your application needs to own the control loop and execution environment.
- Choose the Agents SDK if you want reusable agent definitions and built-in workflow concepts such as handoffs and guardrails.
- Consider the Agents API if durable managed sessions and the documented managed capabilities suit the workload.
Before committing, map the decision to a real workflow: who stores state, who executes each tool, who can approve a sensitive action, and what happens after a timeout or failure? If those answers are unclear, settle the architecture before adding more agents.
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Build a narrow version that can be tested end to end. Each step below produces a concrete design decision rather than a broad instruction to “add AI.”
- Write one job statement. Specify the user outcome, allowed actions, forbidden actions, and completion condition. “Summarize a submitted issue and propose a category” is easier to test than “handle customer problems.” Decide whether the agent may only draft an action or may execute it.
- Define one focused agent. Choose its model, instructions, tools, output format, guardrails, and approval policy. Keep the responsibilities coherent: an agent with one clear task is easier to evaluate and secure than a general agent with broad access.
- List the data and tools the task actually needs. For every operation, identify what information it reads, what it can change, and whether a human must approve it. Do not grant a tool simply because the runtime supports it.
- Decide how context persists. Separate information required for the current turn from durable user or task memory. Pick an approach to continuity—such as sessions, response chaining, or conversations—and define explicit rules for any persistent store.
- Run a complete workflow before expanding it. Test the agent with expected inputs, incomplete information, tool errors, and cases that should be refused or escalated. Confirm that the output meets the completion condition and that disallowed actions remain unavailable.
- Add orchestration only for a reason. Use a handoff if a specialist should take ownership of the next turn. Expose a specialist as a tool if a manager should retain control of the user-facing response.
- Instrument, secure, and test for production. Trace tool calls, handoffs, failures, latency, and final outputs. Test schemas, permission boundaries, retries, malformed data, and recovery; secure external connections with least-privilege credentials and approval for sensitive operations.
What makes a good completion condition?
A completion condition states what evidence should be present before the agent says it is done. For a drafting task, it might require a categorized issue and a proposed reply, without sending anything. For a task that uses external information, it might require the relevant result to have been retrieved and checked before a response is produced. Keep this criterion separate from stylistic instructions: “be concise” describes presentation, while “include a category and a draft” describes completion.
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Why keep the first version small?
A focused agent creates a manageable unit for testing. If it produces a wrong answer, you can inspect its instructions, available tools, state, and execution trace without also debugging several agents’ messages and handoffs. OpenAI’s guidance recommends starting with the smallest agent that owns a clear task. Expand only after a real ownership, tool-access, or approval need appears.
How should you give an agent tools safely?
Give the agent the narrowest tool set that can complete its assigned job, and treat each tool’s definition and permissions as part of the application’s security design. Tool access determines which external actions an agent can attempt; a convincing instruction is not a substitute for enforcing access in the tool or runtime.
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|---|---|---|
| Function tools | Wrap application code that the agent can call. | Define a clear input and output contract, and enforce authorization in the application code. |
| Hosted tools | Provide capabilities such as web search or file search. | Decide what information the tool can access and how its results will be used or checked. |
| Remote MCP connections | Connect the agent to external systems through MCP. | Assess the server, authentication, reachable systems, and the specific permissions granted. |
| Shell or computer tools | Allow interaction with a shell or a computer environment. | Apply especially careful permissions and boundaries because these tools can enable consequential actions. |
The Model Context Protocol (MCP) documentation describes MCP as “an open protocol that standardizes how applications provide context to LLMs” and compares it to a USB-C port for AI applications. In practical terms, MCP gives applications a standard way to make context or tools available; it does not remove the need to assess and trust the particular server or connection.
How do you choose an MCP transport?
Documented options include hosted MCP tools, Streamable HTTP, HTTP with SSE, and local stdio. Choose according to where the server runs, whether it is reachable over the network, how it authenticates, and whether you trust it to receive the requested context or perform the requested action. The transport is an operational choice; it does not itself establish that a server is safe.
What safeguards should tool connections have?
- Trust a server before connecting to it, and review which systems and data it can reach.
- Use least-privilege credentials so the connection has only the access the task requires.
- Keep tokens out of URLs.
- Require approval for sensitive operations rather than relying only on agent instructions.
- Test what happens when a tool receives invalid input, returns malformed data, or cannot complete the operation.
How should you design memory and state?
Separate transient turn context from durable user or task memory. Current-turn context helps the agent handle the active interaction; persistent memory is information intentionally retained and retrieved later. Each has different storage and retrieval needs, so decide what belongs in each rather than treating all conversation history as one undifferentiated memory.
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| State type | Purpose | Design questions |
|---|---|---|
| Transient turn context | Supports the active request and the immediate sequence of tool calls and results. | What context is needed to complete this turn? How does the runtime maintain continuity while the turn is in progress? |
| Session or conversation continuity | Connects interactions within a continuing session or conversation. | Which session, response-chaining, or conversation approach fits the runtime, and what is its intended scope? |
| Durable memory | Preserves selected user or task information for future retrieval. | What may be written, when should it be retrieved, and how can stale or irrelevant information be corrected? |
Sessions, response chaining, and conversations are documented approaches to continuity; a persistent store with explicit retrieval and write rules is the separate design choice for long-term memory. A runtime’s ability to maintain a durable session does not, by itself, decide which user facts should be saved. Define those rules in the application and test them with examples: information that should be retained, information that should not, and later requests where retrieval would or would not be relevant.
One useful operational model comes from LangChain’s Agent Protocol documentation: production serving can be organized around runs, threads, and a store for long-term memory. A run represents work being carried out, a thread provides a continuity boundary, and a store supports longer-lived memory. The labels are helpful, but the implementation should still make ownership, retention, and retrieval behavior explicit.
When should you use multiple agents?
Use multiple agents when there is a clear reason to separate ownership, tool access, or approval policy—not merely because a task has several steps. A multi-agent workflow adds coordination and more points of failure, so keep a single focused agent when it can handle the task within one clear permission boundary.
Use a handoff when a specialist should own the next turn
A handoff transfers control to a specialist when that specialist should take responsibility for continuing the workflow. This can make sense when the next task has a distinct role or different access policy. Define what context is passed, what the specialist may do, and how the workflow returns or reports completion.
Use an agent as a tool when the manager should retain control
If a manager agent should remain responsible for the user-facing response, a specialist can instead be exposed as a tool. The manager can request a bounded contribution and retain responsibility for integrating it into the final answer. This pattern is different from a handoff: the key question is who owns the next turn and the final response.
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For every proposed split, ask whether it creates a real boundary: a separate task owner, a different tool set, or a distinct approval rule. If not, the split may add coordination cost without clarifying responsibility.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you make an agent reliable in production?
Production readiness comes from observable behavior, tested boundaries, and a plan for failure—not from a fluent demonstration on one successful input. OpenAI’s 2025 agent launch materials describe integrated observability as a building block, while its SDK and Agents API documentation cover workflow controls and recovery capabilities. The application still needs tests and policies tailored to its own tools and consequences.
Trace the full workflow
Record enough to understand what happened: tool calls, handoffs, failures, latency, and final outputs. Traces should help distinguish whether a problem came from instructions, a tool contract, unavailable data, a bad handoff, or a final response that did not meet the completion condition. Protect sensitive data in telemetry according to the application’s own requirements.
Test failure cases, not only the happy path
Include tests for tool schemas, permission boundaries, retries, malformed data, and recovery. Also test inputs that are incomplete, ambiguous, or outside the agent’s allowed job. Define what the system should do when a tool fails or returns unusable data—such as retrying where appropriate, asking for missing information, or escalating—rather than treating a plausible answer as proof the operation succeeded.
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Put approval and recovery rules outside the model’s judgment
For consequential operations, enforce approval requirements in the application or runtime. Decide which actions require a human, what information the reviewer sees, and how the workflow resumes after approval or rejection. Recovery should also be explicit: a failed tool call should not silently become a claim that the action was completed.
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What should you compare before choosing an implementation?
Compare architectures across the responsibilities that determine control and operational burden. The categories below apply whether you use one agent, a managed runtime, or an orchestration framework.
| Comparison axis | Question to answer |
|---|---|
| Agent loop | Does your application control each model turn and tool execution, or does an SDK or managed API handle orchestration? |
| State storage | Where does turn context live, how is conversation continuity maintained, and where is durable memory stored? |
| Tool execution | Are tools application functions, hosted capabilities, remote MCP connections, or shell/computer access? |
| Handoffs | Are handoffs a first-class workflow feature, or will your application implement ownership changes? |
| Approvals and guardrails | Where are sensitive operations gated, and which constraints are enforced beyond the agent’s instructions? |
| Observability | Can operators trace tool calls, handoffs, failures, latency, and outputs? |
| Deployment and sandbox | Who owns the execution environment and sandbox boundaries? |
| Recovery | What handles timeouts, tool failures, malformed results, and resumption after interruption? |
Do not choose based on feature count alone. The right fit is the one whose control model, security responsibilities, and recovery behavior match the application you intend to operate.
Frequently Asked Questions
Can an AI agent work without internet access?
It can, if the model runtime and the tools needed for its task are available in the chosen environment. Network-dependent tools such as remote services will not be usable without connectivity, so design the workflow to handle their absence rather than assuming every capability is always reachable.
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Should an agent save every conversation as memory?
No. Durable memory should have explicit write and retrieval rules, because information useful for one request may be irrelevant or inappropriate to retain. Decide what the application is allowed to remember and how that information can be corrected or excluded.
What should happen when an agent’s tool call fails?
The workflow should distinguish a failed operation from a completed one, then follow a defined recovery path such as an appropriate retry, a request for missing information, or escalation. Trace the failure and test the recovery behavior with malformed results and unavailable tools.
Can an MCP server be trusted just because it uses a standard protocol?
No. MCP standardizes how applications provide context to language models, but it does not certify a particular server or its permissions. Assess the server, its authentication, the systems it can reach, and the credentials you grant before connecting.
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