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Build an AI agent as a small, testable software system—not as a clever prompt. Define one job, give a model tightly scoped tools, choose who owns the execution loop, separate temporary conversation state from durable memory, and add validation, approvals, tracing, tests, and recovery before expanding the design. A single focused agent is usually the right starting point. Add specialist handoffs or a manager that calls other agents only when separate ownership, permissions, or approval policies justify the coordination cost.
What is an AI agent, and why is a prompt alone not enough?
An AI agent is a model operating inside a runtime that supplies instructions, tools, state, and rules for deciding what to do next. The model may plan a step, call a tool, inspect the result, revise its plan, and continue until a defined completion condition is met. A prompt can describe a role, but it does not by itself provide authenticated access to business systems, durable state, permission boundaries, retries, or a way to verify the final result.
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Think of the agent as a controlled loop around a model:
- Instructions define the job, constraints, tone, and stopping rule.
- Tools let it read data or take actions outside the model.
- Runtime owns turn handling, tool execution, context, and failures.
- State carries the current task and, when justified, durable user or business memory.
- Controls validate inputs and outputs, require approval for risky actions, and record what happened.
The practical design question is not “How do I make the model autonomous?” It is “Which decisions may the model make, which operations may it request, and what software verifies each operation?”
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Which AI-agent runtime should you choose?
Choose the runtime according to who should own the loop, state, tool execution, and recovery. The three OpenAI runtime patterns serve different control requirements rather than representing a simple beginner-to-advanced ladder.
| Runtime | Best fit | Application owns | Managed capabilities |
|---|---|---|---|
| Responses API | A custom application loop with maximum control | History, tool dispatch, retries, execution environment, and stopping logic | Model responses and available built-in capabilities; your application composes the workflow |
| Agents SDK | Reusable agent definitions and code-managed workflows | Application architecture and deployment | Agent turns, tools, handoffs, guardrails, structured outputs, and tracing patterns |
| Agents API | Durable managed sessions and long-running work | Business-specific tools, policies, and user experience | Orchestration, context compaction, recovery, tools, MCP connections, and optional sandboxes |
OpenAI announced the Responses API, built-in web search, file search, computer use, the Agents SDK, and integrated observability on March 11, 2025 as building blocks for agentic applications. Treat that announcement as a capability baseline, then verify the current API behavior and limits in the documentation for the version you deploy.
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Use the Responses API when your product already has a workflow engine or needs custom persistence, queueing, and sandboxing. Use the Agents SDK when you want explicit agent objects, handoffs, guardrails, and structured results without implementing every turn-management detail yourself. Use the Agents API when durable sessions, compaction, recovery, and managed connections are more valuable than owning every infrastructure decision.
How do you define an agent that can actually finish a job?
Start with one job statement that names the outcome, allowed actions, forbidden actions, and completion test. If a human reviewer cannot tell whether the agent is finished, the agent cannot reliably tell either.
Write the job statement
Use a format such as: “Given input, produce output by performing allowed actions; never perform forbidden actions; finish only when observable condition is true.” For example, a support-triage agent might classify an incoming ticket, look up the customer record, draft a response, and stop for approval before sending anything. Its forbidden actions could include changing billing data, deleting records, or contacting a customer without approval.
Define the output contract
Prefer a small structured result over an unconstrained paragraph. Specify required fields, allowed enum values, whether evidence is required, and what the agent should return when information is missing. A validator should reject malformed output and send the turn back for correction or route it to a human.
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Set boundaries before adding tools
- List the systems the agent may read and the systems it may change.
- Separate read-only operations from mutations.
- State which actions always require approval.
- Define a timeout, maximum turn count, and maximum tool-call budget.
- Specify what “cannot complete” looks like and how that state is reported.
What is the practical build sequence for a first agent?
Build in small increments and make each increment observable. The following sequence keeps model behavior, application code, and permissions understandable.
- Choose the model and runtime. Match the model’s reasoning and latency characteristics to the job, then decide whether your application, the SDK, or a managed API owns the loop.
- Write instructions and a completion condition. Include the job statement, operating constraints, escalation behavior, output schema, and examples of acceptable and unacceptable results.
- Implement one read-only tool. Give it a narrow name, typed arguments, a bounded result, and deterministic error responses. Test it independently of the model.
- Run a single turn. Log the request, tool arguments, tool result, final output, latency, and token usage or equivalent runtime metrics. Do not add memory or multiple agents yet.
- Add the minimum mutation path. Wrap writes in an application function that rechecks authorization and validates the requested change. Return a confirmation object rather than silently assuming success.
- Separate state layers. Keep current-turn context in the session or response chain; put durable facts in a store with explicit read and write rules.
- Add validation and approvals. Validate tool arguments before execution, validate tool results before they re-enter the model, and pause for human approval where impact or uncertainty warrants it.
- Instrument, test, and set recovery limits. Trace every tool call and handoff, test malformed data and permission failures, cap retries and turns, and define a safe terminal state.
Only after this version works should you introduce specialist agents, background jobs, or broad tool access. A larger graph can hide a basic instruction, schema, or authorization defect.
How should you design tools for an AI agent?
A tool is an API contract and a security boundary, not merely a function the model can call. Keep each tool narrow enough that its name, description, arguments, and result make one safe operation obvious.
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Function tools
Function tools wrap application code such as a database lookup, ticket search, or calendar operation. The wrapper should enforce authentication, authorization, input validation, rate limits, and idempotency. Never rely on the model to supply a trustworthy user ID, account scope, or permission level; derive those values from the authenticated request and server-side context.
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{
"name": "find_ticket",
"description": "Return one ticket visible to the signed-in operator.",
"parameters": {
"type": "object",
"properties": {
"ticket_id": {"type": "string", "minLength": 1}
},
"required": ["ticket_id"],
"additionalProperties": false
}
}The application should still verify that the signed-in operator may view the requested ticket. The schema improves model behavior; it does not replace authorization.
Hosted tools
Hosted capabilities such as web search or file search can provide information without you implementing the underlying retrieval service. Define what sources are acceptable, how fresh information must be, and what the agent should do when no reliable result is found. Treat retrieved text as untrusted input: it may contain instructions that conflict with the agent’s policy.
Remote MCP tools
Model Context Protocol (MCP) standardizes how applications provide context and tools to language models. Its documentation compares MCP to a USB-C port for AI applications. An MCP server can expose tools or resources over hosted connections, Streamable HTTP, HTTP with SSE, or local stdio; the right transport depends on where the server runs, network reachability, authentication, and operational trust.
Shell and computer tools
Shell and computer-use tools can affect files, accounts, or external systems at a much larger blast radius than a read-only function. Place them in an isolated environment, grant the smallest possible filesystem and network scope, require approval for consequential actions, and record every command or interaction. If a task can be completed with a typed function, prefer the typed function.
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| Tool type | Good first use | Main risk | Required control |
|---|---|---|---|
| Read-only function | Fetch a record or calculate a value | Data leakage or an overly broad query | Scoped authorization, bounded results, schema validation |
| Mutation function | Create or update one business object | Irreversible or duplicate changes | Idempotency key, recheck, approval policy, audit log |
| Hosted search or retrieval | Find current information in approved sources | Untrusted or stale content | Source policy, citation/evidence requirement, injection filtering |
| MCP connection | Integrate a separately operated tool server | Compromised server or excessive credentials | Server trust review, least privilege, token hygiene, approvals |
| Shell or computer use | Tasks that truly require an interactive environment | High-impact side effects | Sandbox, network restrictions, human confirmation, detailed trace |
How do memory and state work in an AI agent?
Separate transient turn context from durable memory. The first supports the current run; the second stores selected facts or artifacts that should survive a session and be retrieved later.
Transient context
Transient context includes the current user message, recent tool results, intermediate decisions, and task metadata. Sessions, response chaining, or conversations can maintain continuity. Keep only the information needed for the next decision, and summarize or compact older turns when the context grows. A compacted summary should preserve commitments, unresolved questions, identifiers, and evidence links rather than copying every conversational detail.
Durable memory
Durable memory belongs in a persistent store with explicit retrieval and write rules. Useful records might include a user’s stated preference, a project’s approved terminology, or a task artifact. Do not write every model inference as fact. Store the source, timestamp, confidence or status, and an owner when those fields matter, and provide a way to correct or delete the record.
Define read and write policies
- Read policy: retrieve only memories relevant to the current task and authorized for this user or tenant.
- Write policy: save only explicit preferences, verified outcomes, or information the product has promised to remember.
- Conflict policy: prefer newer verified information, ask when two records disagree, and never silently overwrite regulated or high-impact data.
- Privacy policy: exclude secrets and unnecessary personal data, set retention limits, and honor deletion requests.
LangChain’s Agent Protocol describes production serving around runs, threads, and a store for long-term memory. Those concepts map cleanly to most architectures: a run is one execution, a thread groups related interaction, and the store holds durable records. You can implement the same separation with your own database and queue; the labels are less important than keeping lifetimes and permissions distinct.
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When should you use handoffs or multiple agents?
Use multiple agents only when a split gives you clear ownership, different tool permissions, separate approval rules, or an independently testable specialty. Otherwise, coordination adds latency, context-transfer failures, and more places for an unsafe instruction to enter.
Handoffs
With a handoff, a triage agent transfers responsibility for the next turn to a specialist. This suits a system in which the specialist should own the user-facing response and its policy. Pass a small, typed handoff payload: the user goal, verified facts, unresolved questions, and authorization context. Do not pass an unbounded transcript when a structured summary will do.
Agent as a tool
Expose a specialist as a tool when a manager should retain control of the conversation and final answer. The manager can call a research, billing, or planning specialist, inspect the structured result, and decide whether another call or approval is needed. This pattern is useful when the manager owns tone, prioritization, and escalation.
| Question | Prefer a single agent | Prefer a split |
|---|---|---|
| Do tools and permissions differ? | No; one policy can safely cover the job | Yes; each specialist can receive only its required access |
| Who should answer the user? | The same agent that performed the work | Use a handoff when the specialist owns the next turn; use an agent-as-tool when a manager owns the response |
| Can each part be tested independently? | The workflow is short and cohesive | Specialists have stable contracts and separate success criteria |
| What is the failure cost? | One controlled recovery path | Coordination and partial-failure handling are worth the isolation |
Give every handoff a termination rule. A manager that can call specialists indefinitely needs a maximum depth, turn budget, and fallback response when no specialist can complete the request.
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Trust the server before connecting, use least-privilege credentials, keep tokens out of URLs, and require approval for sensitive operations. These controls apply whether a tool is supplied by your own team or an external operator.
- Review the server. Identify its operator, code or deployment provenance, data flows, logging practices, and update process.
- Create a dedicated identity. Scope credentials to the required tenant, resources, actions, and time period. Do not reuse a human administrator token.
- Choose transport deliberately. Use local stdio for a process on the same host, or a network transport when remote reachability and authentication are required. Document the trust boundary for either choice.
- Protect secrets. Keep tokens in a secret manager or protected runtime configuration; never place them in query strings, prompts, logs, or tool arguments.
- Gate side effects. Require a human or policy approval before sending messages, moving money, changing access, deleting data, or performing other sensitive operations.
- Monitor the connection. Record server identity, requested tool, arguments after redaction, result status, latency, and approval decision.
Retrieved documents and MCP responses are data, not authority. Your application must distinguish instructions from content and prevent a document from changing the agent’s permission policy.
How do you make an agent reliable in production?
Reliability comes from controls around the model: typed interfaces, guardrails, approvals, traces, tests, bounded retries, and recovery states. A fluent answer is not evidence that the requested action succeeded.
Trace the complete run
Record the run or thread identifier, model and instruction version, tool calls, handoffs, validation results, approvals, errors, latency, and final status. Redact secrets and personal data according to your retention policy. Traces let you distinguish a reasoning error from a tool outage or an authorization failure.
Test beyond the happy path
- Valid and invalid tool arguments, including extra fields and boundary values.
- Empty, stale, duplicated, malformed, or contradictory tool results.
- Expired credentials, denied permissions, rate limits, timeouts, and partial outages.
- Prompt-injection text in retrieved files, web pages, tickets, and MCP responses.
- Duplicate delivery of the same event and retries after a timeout.
- Handoff loops, context-compaction errors, and a specialist that returns an unusable result.
- Final outputs that violate the required schema, omit evidence, or claim an action that the trace does not show.
Use guardrails and approvals
Input guardrails can reject requests outside the product’s scope. Tool guardrails can enforce schemas, authorization, rate limits, and data-loss rules. Output guardrails can check format, required evidence, and prohibited content. Approval is appropriate when the impact is high, the operation is irreversible, or confidence cannot be established automatically; it is not a substitute for authorization checks in code.
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Make retries safe
Retry only transient failures, with a bounded count and backoff. Mutating operations need an idempotency key or an equivalent deduplication mechanism so a timeout does not create two orders, messages, or tickets. If the system cannot determine whether a mutation succeeded, expose an “unknown—verify” state instead of repeating the action blindly.
Design recovery paths
Define explicit terminal states such as completed, needs approval, needs user information, blocked by policy, failed and retryable, and failed permanently. Preserve the trace and the last verified state when handing work to a human or a background queue. Recovery should resume from a known checkpoint rather than replaying every previous side effect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a production architecture look like?
A practical architecture has a user-facing entry point, an agent runtime, tool adapters, state stores, a policy layer, and observability. The model proposes steps; application code authorizes and executes them.
- Entry point: authenticates the user, establishes tenant and request context, and creates a run or thread.
- Runtime: loads instructions and permitted tools, calls the model, dispatches validated tool requests, and enforces turn and time budgets.
- Policy layer: checks authorization, approval requirements, data handling, and output rules before and after each tool call.
- Tool adapters: translate typed requests into service calls, normalize errors, and return bounded results.
- State: stores transient run data separately from durable memory and task artifacts.
- Queue or worker: handles long-running work and resumes from checkpoints rather than holding a user request open indefinitely.
- Observability: captures traces, metrics, alerts, and an audit record suitable for incident review.
Keep model instructions, tool schemas, and policy code versioned together. A change to a tool’s meaning is an interface change even if the function name remains the same.
What are the most common failure modes?
Most production incidents come from unclear ownership or an unchecked boundary rather than from the model’s inability to generate text.
| Symptom | Likely cause | Fix |
|---|---|---|
| The agent claims success but nothing changed | No post-action verification | Require the tool to return a confirmed result and make the final response depend on it |
| Duplicate records or messages | Retrying a mutation after an ambiguous timeout | Add idempotency and an unknown-outcome recovery state |
| Irrelevant or stale answers | Unbounded retrieval or old durable memory | Apply relevance, authorization, freshness, and conflict rules before context injection |
| Data crosses tenants | Trusting model-supplied scope identifiers | Derive scope from authenticated server context and enforce it in every adapter |
| Agents loop or hand off forever | No depth, turn, or completion limit | Set hard budgets and a human or safe-failure escape path |
| Tool calls are unsafe | Broad schemas or credentials | Split operations, narrow arguments, use least privilege, and require approval for impact |
How should you decide whether to expand the system?
Expand only when a measured limitation has a clear architectural remedy. If one agent is failing because instructions are ambiguous, fix the contract before adding another agent. If it needs incompatible permissions, separate approval policies, or a specialist that can be tested and deployed independently, a handoff or agent-as-tool boundary may be justified.
Review traces and test outcomes for evidence: repeated authorization conflicts, a stable specialist interface, excessive context size, or a queue requirement are stronger reasons to split than a desire for a more impressive diagram. Keep the smallest topology that meets the job’s reliability and safety requirements.
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Do I need a vector database to give an agent memory?
No. Durable memory can be a relational table, document store, or another persistent system if it supports the retrieval, authorization, retention, and correction rules your product needs. Choose an embedding index only when similarity retrieval is actually useful; it does not replace a source of truth or a deletion policy.
Should every tool call require human approval?
No. Requiring approval for harmless, read-only lookups can make an agent unusable. Apply approval to actions with meaningful financial, legal, privacy, safety, or reputational impact, and let policy and authorization checks protect lower-risk operations automatically.
How can I change an agent without breaking active sessions?
Version instructions, tool schemas, and output contracts, then pin each run or thread to the version that created it. Migrate durable memory with an explicit compatibility step, and keep old versions available long enough to finish in-flight work or replay it safely.
What should the user see when a tool is unavailable?
Show a precise status such as “This lookup is temporarily unavailable; no change was made,” rather than an invented answer. Preserve the request and verified context so the user can retry or authorize a different path without repeating side effects.
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Compare traces for completion rate, escalation rate, latency, tool errors, and approval burden before and after the split. If the specialist does not improve a defined outcome or safety boundary, remove the coordination layer and keep the simpler single-agent design.
Frequently Asked Questions
Do I need a vector database to give an agent memory?
No. Durable memory can be a relational table, document store, or another persistent system if it supports the retrieval, authorization, retention, and correction rules your product needs. Choose an embedding index only when similarity retrieval is actually useful; it does not replace a source of truth or a deletion policy.
Should every tool call require human approval?
No. Requiring approval for harmless, read-only lookups can make an agent unusable. Apply approval to actions with meaningful financial, legal, privacy, safety, or reputational impact, and let policy and authorization checks protect lower-risk operations automatically.
How can I change an agent without breaking active sessions?
Version instructions, tool schemas, and output contracts, then pin each run or thread to the version that created it. Migrate durable memory with an explicit compatibility step, and keep old versions available long enough to finish in-flight work or replay it safely.
What should the user see when a tool is unavailable?
Show a precise status such as “This lookup is temporarily unavailable; no change was made,” rather than an invented answer. Preserve the request and verified context so the user can retry or authorize a different path without repeating side effects.
How do I know whether a second agent is helping?
Compare traces for completion rate, escalation rate, latency, tool errors, and approval burden before and after the split. If the specialist does not improve a defined outcome or safety boundary, remove the coordination layer and keep the simpler single-agent design.
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