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Written by Jacob Sipos
Published Sep 9, 2026
Build AI Agents
An agent is more than a chatbot with a fancy name.
An AI agent can understand a goal, decide what information it needs, choose tools, take actions, inspect the result, and continue working until it reaches a stopping point.
The exciting part is not giving AI more freedom for the sake of freedom. It is building a system that can handle a useful job within clear boundaries you designed.
AGENT CORE · GOAL + REASONING + TOOLS
AI
AGENT
AGENT
Instructions
Tools
Context
Guardrails
First, What Is an AI Agent?
An AI agent is software that uses an AI model to pursue a goal and decide which actions to take along the way. A normal chatbot usually responds to a prompt. An agent can go further by using tools, reading information, changing state, calling APIs, working with files, handing work to another agent, or repeating steps based on what happened.
Current agent frameworks emphasize these same building blocks. OpenAI’s Agents SDK includes configurable agents, tools, handoffs, guardrails, and tracing. Microsoft’s current agent guidance describes agents as systems that interpret intent, plan work, use tools, and take action, while Anthropic’s current guidance distinguishes single agents, multi-agent systems, and more deterministic workflows. citeturn967513search0turn967513search2turn967513search5
You ask a chatbot: “Which customers look at risk?” It gives you an answer based only on the information in the conversation.
You give an agent the same job. It may inspect approved CRM data, review unresolved support issues, check recent usage, compare the accounts against your risk rules, prepare a summary, and ask you to approve the next step.
The Agent Difference
An agent is useful when the AI needs to decide what to do next, not merely generate the next paragraph.
Before You Build an Agent, Ask Whether a Normal Workflow Would Be Better
PROBLEM
VARIATION
TOOLS
JUDGMENT
AGENT?
This may be the most important design decision.
Use a normal workflow
When the process has fixed, predictable steps. “When a form arrives, save the PDF and email a receipt” does not require autonomous reasoning.
Consider an agent
When the path changes depending on what the system discovers, the task is open-ended, or the AI needs to decide among several tools or strategies.
Microsoft’s July 2026 Agent Framework guidance is unusually direct: if you can reliably solve the task with a normal function, use the function instead of an AI agent. citeturn967513search3
More autonomy is not automatically more sophisticated. The best architecture is the simplest one that reliably solves the problem.
Give the Agent a Job Description, Not a Vague Dream
AGENT SPECIFICATION
ROLECustomer Risk Analyst
GOALFind accounts that need attention
BOUNDARYNever contact customers directly
OUTPUTEvidence-backed risk brief
CORE INSTRUCTIONS
Review only approved CRM, billing, product-usage, and support data. Identify accounts with at least two material risk indicators. Explain the evidence. If information conflicts or confidence is low, flag the account for human review. Do not modify customer records or send communications.
A good agent needs clear scope. Tell it what success means, which sources it may trust, what it must never do, what output format you expect, and when it should stop.
Useful test: If a new employee received these instructions, would they know what the job is? If not, the agent probably will not either.
An Agent Becomes Useful When It Can Work With the Real World
⌕Search knowledgeREAD
CRMRead customer recordREAD
✓Create taskWRITE
@Draft emailDRAFT
→
AGENT DECISION
Use the CRM tool first.
The request is about a known customer. Retrieve the approved customer record before checking support history.
A tool might be a database lookup, calendar action, email draft, web search, file reader, API, calculator, code execution environment, or business system.
Modern agent frameworks increasingly support large tool ecosystems. OpenAI’s Agents SDK and Anthropic’s developer tooling both emphasize dynamic tool use, while Microsoft agents can combine tools, skills, APIs, workflows, and MCP-connected services. citeturn967513search1turn967513search4turn967513search6
The agent should receive the smallest useful set of permissions. If it only needs to read invoices, do not give it permission to issue refunds.
Context Tells the Agent What Matters Right Now. Memory Helps It Remember What Matters Later.
REFERENCECompany refund policy
Approved source material available when needed.
SESSIONCurrent customer conversation
What has already happened during this task.
MEMORYUseful persistent preference
GIVE THE AGENT THE RIGHT CONTEXT, NOT ALL POSSIBLE CONTEXT
Customer prefers morning appointments and email follow-up.
Context can include the user’s request, current workflow state, retrieved documents, tool results, policies, customer records, or other information relevant to the current decision.
Memory is information preserved across interactions when the application genuinely benefits from persistence.
More memory is not automatically better. Old, irrelevant, incorrect, or overly broad information can make an agent worse. Good context management includes deciding what should be loaded, summarized, forgotten, or retrieved only when needed.
The Agent Loop Is the Engine Behind the Behavior
Think → Act → Observe → Decide
1
Understand the current state
Understand the current state
2
Choose a tool or action
Choose a tool or action
3
Inspect what happened
Inspect what happened
4
Continue, stop, or escalate
Continue, stop, or escalate
The loop repeats until the goal is complete, a stopping rule is reached, or human input is required.
An agent is asked to schedule a meeting with three people.
It checks availability. No shared opening exists this week. Instead of failing, it checks the next week. It finds two options, applies the user’s preference for afternoons, and presents the best one for approval.
That ability to react to tool results is what makes agentic behavior different from executing a fixed list of steps.
A Good Agent Does Not Need Permission to Do Everything
APPROVAL REQUIRED
Refund recommendation
Order #8392
Policy window: Eligible
Requested amount: $187
Reason: Duplicate charge
Evidence: Payment history confirms duplicate transaction
Policy window: Eligible
Requested amount: $187
Reason: Duplicate charge
Evidence: Payment history confirms duplicate transaction
Approval gates are one of the most useful agent design patterns.
The agent can gather information, check policy, calculate an amount, prepare the action, and then stop before something consequential happens.
This pattern works for refunds, external messages, permission changes, publishing, purchases, customer-account changes, data deletion, contracts, or other actions where human authority should remain explicit.
Automate Preparation Before Authority
If you are unsure how much autonomy is appropriate, let the agent automate the research and preparation first. Add execution only after the workflow has earned trust.
If You Cannot See What the Agent Did, It Is Hard to Improve What the Agent Did
EXECUTION TRACE · RUN 0281
✓ CompletedCustomer risk review
12 tool calls4.8 seconds
1 approvalHuman accepted
TRACE
1 Read CRM record
2 Checked unresolved support cases
3 Compared usage trend
4 Found conflicting billing status
5 Requested human review
OpenAI’s Agents SDK includes tracing and observability specifically so developers can inspect agent runs, debug tool usage, and understand handoffs. Microsoft similarly emphasizes monitoring and governance across agent systems. citeturn967513search2turn967513search5
Useful logs answer questions such as: Which tool was called? What result came back? Why did the agent branch? How long did it take? What did it cost? Where did the error begin? Did a person approve the final action?
Sometimes Several Focused Agents Are Better Than One Giant Agent
Multi-agent orchestration
COORDINATOR
Research AgentFind evidence
Data AgentAnalyze numbers
Policy AgentCheck rules
Writer AgentPrepare brief
A coordinator can delegate specialized work to other agents or hand control to the agent best suited for a task. OpenAI’s Agents SDK supports handoffs, and Anthropic’s current agent architecture guidance covers single-agent, multi-agent, sequential, parallel, and evaluator-optimizer patterns. citeturn967513search0turn967513search5
Multi-agent design is useful when roles are genuinely different. It also creates more moving parts, more context passing, more tool calls, and more ways for mistakes to propagate.
Do not build an “agent team” because it sounds futuristic. Start with one good agent. Split roles only when specialization clearly improves reliability or maintainability.
Do Not Test an Agent Only by Chatting With It Five Times
Evaluation set
Normal cases50
Ambiguous cases20
Missing data15
Adversarial cases15
Current performance
Correct routing
Tool selection
Policy compliance
Escalation quality
Agent evaluation should use repeatable test cases. Measure whether the agent chooses the right tool, follows policy, stops when it should, escalates uncertainty, produces useful output, and avoids prohibited actions.
Test difficult cases too: missing fields, contradictory sources, unavailable tools, malformed inputs, prompt injection attempts, duplicate requests, stale context, and low-confidence results.
An agent that succeeds on the happy path can still be unreliable in production.
Guardrails Are Part of the Product, Not a Final Safety Sticker
✓
Limit tools to what the job needs
Validate inputs and outputs
Require approval for consequential actions
Protect secrets and sensitive data
Set time, cost, and action limits
Log tool calls and decisions
Create a safe failure and escalation path
OpenAI’s Agents SDK includes configurable guardrails for input and output validation, and newer agent platforms emphasize sandboxing, identity, access control, observability, and governance. citeturn967513search1turn967513search2turn967513search5
Also protect the agent from instructions embedded inside untrusted data. A document, webpage, or email the agent reads may contain text telling the agent to ignore its rules. The system should distinguish trusted instructions from untrusted content.
A Practical Way to Build Your First Agent
1. Choose one bounded job.
“Prepare the weekly customer-risk review” is better than “help run the business.”
2. Write the instructions.
Define the goal, approved sources, rules, output, stopping condition, and prohibited actions.
3. Add the minimum tools.
Start read-only when possible. Add write access only when it creates real value.
4. Decide what context it needs.
Give it the relevant policy, user request, records, or retrieved knowledge without flooding it with unrelated information.
5. Add approval gates.
Decide exactly which actions require a person.
6. Create evaluation cases.
Test success, ambiguity, failure, adversarial input, missing tools, and incorrect data.
7. Trace every run.
Make tool calls, errors, approvals, timing, and decisions inspectable.
8. Expand autonomy slowly.
Let the agent earn broader permissions through reliable performance rather than starting with maximum access.
Things People Often Do Not Realize AI Agents Can Do
Choose among several tools without you specifying the exact sequence.
The agent can inspect the current state and decide whether it needs search, a database, a calculator, a file, an API, or another available tool.
Pause halfway through a job and ask for approval.
Human-in-the-loop design lets the agent automate preparation while preserving explicit authority for sensitive actions.
Hand a task to a more specialized agent.
Modern frameworks support handoffs and orchestration between agents with different roles or tool access.
Work inside a controlled computer environment.
OpenAI’s 2026 Agents SDK updates added model-native computer workflows and sandbox execution for agents working across files and commands. citeturn967513search1
Remember useful information across sessions.
Agent systems can include persistent memory when the product needs continuity, although memory should be selective and governed.
Recover from a failed tool call.
A well-designed agent can inspect an error, choose another approach, retry within limits, or escalate instead of simply stopping.
Be evaluated against a repeatable test suite.
Agent quality can be measured using defined scenarios and metrics rather than judged only through casual conversation.
Use hundreds of tools without loading every tool definition at once.
Newer agent tooling supports dynamic tool discovery so agents can locate relevant capabilities when needed instead of carrying the entire tool library in every context. citeturn967513search4
AI Agents Are Tools. Good Architecture Makes Them Valuable.
| Benefit | Helpful Practice |
|---|---|
| Agents can handle open-ended, multi-step work. | Use a deterministic workflow instead when the steps are already known and fixed. |
| Tools let agents act on real systems. | Use least-privilege permissions and separate read, draft, and execute capabilities. |
| Memory can create continuity. | Store only information that genuinely improves future work and govern how it is updated or removed. |
| Agents can recover and adapt when conditions change. | Set retry limits, budgets, stopping conditions, and escalation paths so adaptation does not become endless looping. |
| Multi-agent systems can add specialization. | Use them only when the extra coordination produces measurable value over a simpler design. |
| Agents can automate consequential preparation. | Use human approval before high-impact external actions, financial commitments, security changes, or irreversible operations. |
| Tracing and evaluation can improve reliability. | Monitor real runs, maintain test cases, and re-evaluate after model, prompt, tool, or policy changes. |
A 7 Day AI Agent Challenge
Day 1: Define one job.
Write one sentence describing a useful task that has variable steps but a clear outcome.
Day 2: Write the agent specification.
Define role, goal, sources, rules, output, and prohibited actions.
Day 3: Add one read-only tool.
Let the agent retrieve approved information and explain why it chose that tool.
Day 4: Add an action with approval.
Let the agent prepare an action, then require you to approve before execution.
Day 5: Break it on purpose.
Give missing information, conflicting data, or an unavailable tool and see whether it fails safely.
Day 6: Create ten evaluation cases.
Run the same scenarios repeatedly and record whether the agent behaves consistently.
Day 7: Review the trace.
Inspect every tool call, branch, failure, retry, and approval. Improve the instructions or architecture before adding more autonomy.
Final Thoughts
Building an AI agent is not primarily about giving an AI model as much freedom as possible.
It is about designing a useful job.
The agent needs instructions that define the goal. Tools that let it interact with the world. Context that helps it understand the current situation. Memory only where persistence adds value. Guardrails that define what it may not do. Approval gates that preserve human authority. Tracing that makes the work inspectable. Evaluations that tell you whether it actually performs reliably.
The most important design choice may be recognizing when you do not need an agent at all. A fixed workflow is often cheaper, faster, easier to test, and easier to trust.
Use agents for the work that genuinely benefits from flexible reasoning and tool selection. Keep the boundaries explicit. Give autonomy gradually. Measure what happens. And build the system so the agent knows not only how to act, but when to stop and ask a person.
Disclaimer: This article provides general educational and informational guidance about designing AI agents. Agent frameworks, models, SDKs, tool integrations, memory systems, identity controls, sandboxes, guardrails, permissions, and product features change frequently and can vary by provider, account, environment, administrator settings, region, and rollout. Agent actions and outputs should be tested, monitored, and reviewed at a level appropriate to their consequences. Use approval gates and appropriately qualified professional review for financial, legal, medical, employment, security, privacy, contractual, regulatory, or other high-impact actions. Follow applicable privacy, security, data-retention, intellectual-property, records-management, and organizational policies when connecting agents to data or tools. Product names belong to their respective owners. This independently created educational article is not affiliated with or endorsed by any AI agent provider. All decorative agent, tool, memory, trace, orchestration, evaluation, and guardrail graphics on this page are original HTML and CSS and do not use copied product screenshots, stock imagery, or watermarked artwork.
Official references:OpenAI Agents SDK · Microsoft Agents Hub · Anthropic Agent Architecture Guide
