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Build an AI agent by giving a language model a bounded job, explicit instructions, a small set of controlled tools, and a run loop with a clear stop condition. Start with one agent and one workflow; add routing, specialist agents, memory, or autonomy only when measured failures show that the simpler design is insufficient.
This guide takes you from task definition to a working loop, then covers tool contracts, safety, evaluation, runtime choices, troubleshooting, and operating costs.
What makes an AI agent different from a chatbot?
A chatbot usually produces a response to one user message. An agent controls a workflow: it decides which step to take, calls tools, observes the results, checks whether the task is complete, and either continues, stops, or hands control to a person. A classifier or one-turn prompt is not an agent merely because it uses an LLM.
The smallest useful mental model is model + instructions + tools. The model reasons about the current state, instructions define the objective and boundaries, and tools retrieve information or perform actions. Retrieval, memory, and guardrails can augment that core, but they do not replace a defined task and completion test.
1. Define a bounded job before choosing a framework
Write a one-sentence job specification before writing prompts or code. Include the user’s goal, permitted data, permitted actions, and the evidence required to declare success.
Use case worksheet
- Goal: What outcome should the user receive?
- Inputs: Which fields are required, optional, or untrusted?
- Data boundary: Which files, records, websites, or APIs may the agent read?
- Action boundary: Which actions are read-only, reversible, or sensitive?
- Completion: What observable condition means the job is done?
- Stop and handoff: When must the agent stop, ask a question, or request approval?
If the sequence is predictable, ordinary code or a fixed LLM workflow is usually easier to test and cheaper to run. Agents are most useful when the next step depends on information discovered during execution and cannot be reliably hardcoded.
2. Design instructions and tool contracts
Write instructions as operating rules
State the objective, allowed sources, output schema, uncertainty policy, and stop conditions. Give examples of valid and invalid behavior. Tell the agent to ask for missing information instead of guessing, and to summarize proposed side effects before requesting approval.
Expose a small, typed toolset
Each tool should have a unique name, a narrow purpose, a JSON schema for parameters, documented errors, and an explicit permission level. Return structured data rather than a page of prose. Keep secrets in the tool implementation, not in prompts or user-visible messages.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11| Tool design choice | Safer default | Why it matters |
|---|---|---|
| Parameters | Required fields, enums, length limits | Reduces malformed or over-broad calls |
| Permissions | Read-only first; separate write tools | Makes approval and auditing practical |
| Results | Structured JSON with status and identifiers | Lets the model distinguish success from failure |
| Errors | Stable codes plus safe human text | Supports retries without exposing internals |
| Side effects | Idempotency keys and confirmation | Prevents duplicate or accidental actions |
3. Implement a controlled run loop
Every run needs a maximum number of turns, an error path, and an exit condition. OpenAI’s practical guidance describes a run as a loop that lets agents operate until an exit condition is reached. Do not let a model call tools indefinitely.
Minimal loop in Python
The following example uses an OpenAI-compatible chat endpoint over HTTP, so it needs only Python’s standard library. Set MODEL_API_KEY, and optionally MODEL_URL and MODEL_NAME. The model must support tool calls in the endpoint’s normal format.
import json
import os
import sys
import urllib.request
from datetime import datetime, timezone
MODEL_URL = os.getenv('MODEL_URL', 'https://api.openai.com/v1/chat/completions')
MODEL_KEY = os.environ['MODEL_API_KEY']
MODEL_NAME = os.getenv('MODEL_NAME', 'your-tool-capable-model')
MAX_TURNS = 8
TOOLS = [{
'type': 'function',
'function': {
'name': 'current_time',
'description': 'Return the current UTC time.',
'parameters': {'type': 'object', 'properties': {}, 'additionalProperties': False}
}
}]
INSTRUCTIONS = '''You are a scheduling assistant. Use current_time when the user asks for the current time.
Never claim an action happened unless a tool returned success. Ask a clarifying question when required data is missing.
When the request is complete, answer briefly and do not call another tool.'''
def current_time(_args):
return {'utc': datetime.now(timezone.utc).isoformat()}
def call_model(messages):
payload = {'model': MODEL_NAME, 'messages': messages, 'tools': TOOLS, 'temperature': 0}
request = urllib.request.Request(
MODEL_URL,
data=json.dumps(payload).encode(),
headers={'Authorization': f'Bearer {MODEL_KEY}', 'Content-Type': 'application/json'},
method='POST')
with urllib.request.urlopen(request, timeout=60) as response:
return json.load(response)['choices'][0]['message']
def run(user_text):
messages = [
{'role': 'system', 'content': INSTRUCTIONS},
{'role': 'user', 'content': user_text}
]
for _ in range(MAX_TURNS):
message = call_model(messages)
messages.append(message)
calls = message.get('tool_calls', [])
if not calls:
return message.get('content', '')
for call in calls:
if call['function']['name'] != 'current_time':
raise RuntimeError('Unknown tool requested')
args = json.loads(call['function'].get('arguments') or '{}')
result = current_time(args)
messages.append({
'role': 'tool',
'tool_call_id': call['id'],
'name': call['function']['name'],
'content': json.dumps(result)
})
raise RuntimeError('Maximum turns reached; run stopped safely')
if __name__ == '__main__':
prompt = ' '.join(sys.argv[1:]) or 'What time is it in UTC?'
print(run(prompt))In production, replace the demonstration tool with an adapter that enforces authentication, validates arguments, applies rate limits, and logs the request and result. Treat model output as untrusted input even when the model is instructed to follow a schema.
4. Decide whether one agent is enough
Start with one agent while its tools and instructions remain understandable. Add complexity only after traces show a specific problem.
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Prompt chaining
Use fixed sequential steps when each stage has a known purpose, such as extract facts, validate them, then write a report. Intermediate checks can prevent an early mistake from propagating.
Routing
Route different input classes to specialized processes when their policies or tools differ substantially. Keep the router’s categories explicit and test borderline cases.
Parallelization
Run independent lookups concurrently, or ask independent reviewers to examine the same result. Reconcile outputs with a deterministic rule or a separate evaluator.
Orchestrator and workers
Use a manager that assigns dynamically discovered subtasks when the number and shape of subtasks depend on the request. Define worker contracts and a budget so the manager cannot create unbounded work.
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When quality criteria are clear, let an evaluator score a draft and an optimizer revise it. Store both versions and the evaluation reason so improvements can be measured rather than assumed.
Peer handoffs and specialist agents can clarify ownership when tools overlap or domains are distinct, but each additional agent adds latency, coordination failure modes, and token cost. A single agent with better tools is often the better first iteration.
5. Protect data, tools, and users
Handle prompt injection
Web pages, documents, emails, and tool results may contain text that tries to override instructions. Keep untrusted values out of privileged developer messages. Mark external content as data, constrain what it may influence, and require confirmation for sensitive actions.
Reduce permissions
- Give each tool only the credentials and records it needs.
- Separate read, draft, and execute operations.
- Use allowlists for hosts, file paths, commands, and recipients.
- Validate inputs and outputs against schemas before dispatch.
- Run code and browsing tools in a sandbox with time, network, and storage limits.
- Keep human approval enabled for financial, destructive, privacy-sensitive, or externally visible actions.
Structured outputs, approvals, and guardrails reduce risk but cannot make an agent error-proof. Continue inspecting traces after every prompt, model, tool, or routing change.
6. Evaluate behavior with traces and repeatable cases
A trace records model calls, tool calls, guardrails, handoffs, inputs, outputs, and errors for one run. Trace graders can check whether the agent chose the right tool, stopped at the right time, followed policy, and handed off appropriately.
Build a small regression set
- Normal successful requests
- Ambiguous requests that should trigger a question
- Malformed or unavailable tool results
- Permission-boundary violations
- Prompt-injection attempts in retrieved content
- Transient failures and timeouts
- Requests that must stop or require a human
Record each failure so it can be replayed. Establish a baseline with a capable model, then compare faster or less expensive models against the same acceptance criteria. Promote stable examples into a dataset and run it whenever instructions, tools, models, or orchestration change.
Measure outcomes that matter
Track task success, correct tool selection, policy violations, unnecessary tool calls, handoff quality, latency, token usage, and the rate of runs that hit the turn limit. Measure on your own workload; there is no universal framework or model ranking supported by these design principles.
7. Choose a runtime and state owner
The OpenAI Agents SDK documentation describes a higher-level runtime with instructions, tools, handoffs, guardrails, sessions, human involvement, MCP integrations, and tracing. It is a fit when the runtime should manage turns, dispatch, state, and safety hooks. Use a lower-level Responses API approach when your application should own the loop, tool dispatch, and state, or when the workflow is short-lived. That is a vendor-specific division, not a universal framework verdict.
For any SDK or framework, compare the same dimensions: who owns state and orchestration, how tool schemas are validated, how approvals and sandboxes work, what traces and repeatable evals are available, expected latency and cost, deployment constraints, and your team’s language and runtime fit.
8. Reliability, performance, and cost engineering
- Bound work: Set turn, time, tool-call, and token budgets. Fail closed when a budget is exhausted.
- Make retries safe: Retry network failures with backoff, but use idempotency keys for writes and never blindly repeat a non-idempotent action.
- Cache carefully: Cache immutable retrievals and deterministic intermediate results; include user, permission, and version context in cache keys.
- Control latency: Parallelize independent reads, trim irrelevant context, and avoid sending full tool histories when summaries preserve the needed state.
- Control spend: Count model calls, input and output tokens, tool executions, and human-review time per completed task. Compare models only on the same acceptance set.
- Observe production: Store redacted traces, correlation IDs, tool timing, error codes, and the final disposition. Set alerts for rising failure or handoff rates.
9. Troubleshooting common failures
The agent loops or repeats a tool
Cause: no explicit completion test, ambiguous tool result, or excessive maximum turns. Return a structured success flag, instruct the agent what evidence ends the task, and enforce a turn budget with a safe handoff.
It chooses the wrong tool
Cause: overlapping names or vague descriptions. Rename tools around user intent, document parameters and examples, remove unused tools, and add a trace-grader case for the decision.
It claims success after an error
Cause: errors are returned as prose or ignored by the loop. Use stable error codes, append the tool result to the conversation, and require the final response to cite a successful result or state that the task failed.
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Cause: untrusted text was treated as instructions. Delimit external content, keep policy in a privileged message, filter dangerous fields, and test injection strings in your regression set.
Costs or latency spike
Cause: too many turns, oversized context, serial independent calls, or a model that is larger than the task requires. Inspect traces, cap budgets, parallelize safe reads, summarize state, and test a smaller model against the same quality gates.
A tool call times out
Set a per-tool timeout, return a typed timeout error, retry only safe operations, and let the agent ask the user or hand off rather than waiting indefinitely.
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FAQ
Does an agent need memory?
No. A short-lived run can keep state in its message history or your application database. Add session memory only when a demonstrated requirement—such as multi-step work across visits—justifies its privacy, deletion, and consistency costs.
When should an agent ask a human?
Ask when intent, authorization, or safety is uncertain, or immediately before an irreversible or externally visible action. The approval request should show the proposed action, target, and relevant evidence.
Can I start with multiple agents?
You can, but a single agent makes failures easier to attribute. Split into specialists after traces show persistent tool-selection confusion, separable policies, or independently testable domains.
What should I log?
Log a redacted correlation ID, model and prompt versions, tool arguments and results, guardrail decisions, timing, token counts, handoffs, and final status. Avoid storing secrets or unnecessary personal data.
Frequently Asked Questions
How long does it take to build a first agent?
A bounded read-only prototype can be built in a day or less, but production readiness depends on permissions, sandboxing, evaluation coverage, and operational monitoring rather than the demo’s completion time.
Should an agent always be autonomous?
No. Reliability often improves when the system pauses for clarification or approval. Autonomy is a design choice to validate against task outcomes, not a quality goal by itself.
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Add retrieval when the task depends on external or changing information that is not safely contained in the prompt. Measure whether retrieved context improves your acceptance cases and whether it introduces injection or permission risks.
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