How to Build an AI Agent

How to Build an AI Agent

Short answer: To build an AI agent that works in practice, treat it as a controlled loop: take input, decide the next action, call a narrowly scoped tool, observe the result, and repeat until a clear “done” check passes. It earns its keep when the task is multi-step and tool-driven; if a single prompt solves it, skip the agent. Add strict tool schemas, step limits, logging, and a validator/critic so that when tools fail or inputs are ambiguous, the agent escalates instead of looping.

Key takeaways:

Controller loop: Implement input→act→observe repetition with explicit stop conditions and max steps.

Tool design: Keep tools narrow, typed, permissioned, and validated to prevent “do_anything” chaos.

Memory hygiene: Use compact short-term state plus long-term retrieval; avoid dumping full transcripts.

Misuse resistance: Add allowlists, rate limits, idempotency, and “dry-run” for risky actions.

Testability: Maintain a scenario suite (failures, ambiguity, injections) and rerun on every change.

How to Build an AI Agent? Infographic
Articles you may like to read after this one:

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🔗 How to evaluate AI models
Compare models using tests, rubrics, and real-world task outcomes.

🔗 How to optimize AI models
Improve quality and cost with tuning, pruning, and monitoring.

1) What an AI agent is, in normal-person terms 🧠

An AI agent is a loop. LangChain “Agents” docs

That’s it. A loop with a brain in the middle.

Input → think → act → observe → repeat. ReAct paper (reason + act)

Where:

  • Input is a user request or an event (new email, support ticket, sensor ping).

  • Think is a language model reasoning about the next step.

  • Act is calling a tool (search internal docs, run code, create a ticket, draft a reply). OpenAI Function calling guide

  • Observe is reading the tool output.

  • Repeat is the part that makes it feel “agentic” instead of “chatty”. LangChain “Agents” docs

Some agents are basically smart macros. Others act more like a junior operator that can juggle tasks and recover from errors. Both count.

Also, you don’t need full autonomy. In fact… you probably don’t want it 🙃

2) When you should build an agent (and when you shouldn’t) 🚦

Build an agent when:

  • The work is multi-step and changes depending on what happens mid-way.

  • The job needs tool use (databases, CRMs, code execution, file generation, browsers, internal APIs). LangChain “Tools” docs

  • You want repeatable outcomes with guardrails, not just one-off answers.

  • You can define “done” in a way a computer can check, even loosely.

Don’t build an agent when:

  • A simple prompt + response solves it (don’t over-engineer, you’ll hate yourself later).

  • You need perfect determinism (agents can be consistent-ish, but not robotic).

  • You don’t have any tools or data to connect - then it’s mostly just vibes.

Let’s be frank: half of “AI agent projects” could be a workflow with a few branching rules. But hey, sometimes the vibe matters too 🤷♂️

3) What makes a good version of an AI agent ✅

Here’s the “What makes a good version of” section you asked for, except I’m going to be a bit blunt:

A good version of an AI agent is not the one that thinks the hardest. It’s the one that:

If your agent can’t be tested, it’s basically a very confident slot machine. Fun at parties, terrifying in production 😬

4) The core building blocks of an agent (the “anatomy” 🧩)

Most solid agents have these pieces:

A) The controller loop 🔁

This is the orchestrator:

B) Tools (aka capabilities) 🧰

Tools are what make an agent effective: LangChain “Tools” docs

  • database queries

  • sending emails

  • pulling files

  • running code

  • calling internal APIs

  • writing to spreadsheets or CRMs

C) Memory 🗃️

Two kinds matter:

  • short-term memory: the current run context, recent steps, current plan

  • long-term memory: user preferences, project context, retrieved knowledge (often via embeddings + a vector store) RAG paper

D) Planning and decision policy 🧭

Even if you don’t call it “planning”, you need a method:

E) Guardrails and evaluation 🧯

Yes, it’s more engineering than prompting. Which is… kind of the point.

5) Comparison Table: popular ways to build an agent 🧾

Below is a realistic “Comparison Table” - with a few quirks, because real teams are quirky 😄

Tool / Framework Audience Price Why it works Notes (tiny chaos)
LangChain builders who like lego-style components free-ish + infra big ecosystem for tools, memory, chains can get spaghetti-fast if you don’t name things clearly
LlamaIndex RAG-heavy teams free-ish + infra strong retrieval patterns, indexing, connectors great when your agent is basically “search + act”… which is common
OpenAI Assistants style approach teams wanting faster setup usage-based built-in tool calling patterns and run state less flexible in some corners, but clean for many apps OpenAI Runs API OpenAI Assistants function calling
Semantic Kernel devs who want structured orchestration free-ish neat abstraction for skills/functions feels “enterprise tidy” - sometimes that’s a compliment 😉
AutoGen multi-agent experimenters free-ish agent-to-agent collaboration patterns can over-talk; set strict termination rules
CrewAI “teams of agents” fans free-ish roles + tasks + handoffs are easy to express works best when tasks are crisp, not mushy
Haystack search + pipelines people free-ish solid pipelines, retrieval, components less “agent theater”, more “practical factory”
Roll-your-own (custom loop) control freaks (affectionate) your time minimal magic, maximum clarity usually the best long-term… until you reinvent everything 😅

No single winner. The best choice depends on whether your agent’s main job is retrieval, tool execution, multi-agent coordination, or workflow automation.

6) How to Build an AI Agent step-by-step (the actual recipe) 🍳🤖

This is the part most people skip, then wonder why the agent behaves like a raccoon in a pantry.

Step 1: Define the job in one sentence 🎯

Examples:

  • “Draft a customer reply using policy and ticket context, then ask for approval.”

  • “Investigate a bug report, reproduce it, and propose a fix.”

  • “Turn imperfect meeting notes into tasks, owners, and deadlines.”

If you can’t define it simply, your agent can’t either. I mean it can, but it will improvise, and improvisation is where budgets go to die.

Step 2: Decide autonomy level (low, medium, spicy) 🌶️

  • Low autonomy: suggests steps, human clicks “approve”

  • Medium: runs tools, drafts output, escalates on uncertainty

  • High: executes end-to-end, only pings humans on exceptions

Start lower than you want. You can always crank it up later.

Step 3: Pick your model strategy 🧠

You typically choose:

  • one strong model for everything (simple)

  • one strong model + smaller model for cheap steps (classification, routing)

  • specialized models (vision, code, speech) if needed

Also decide:

  • max tokens

  • temperature

  • whether you allow long reasoning traces internally (you can, but don’t expose raw chain-of-thought to end users)

Step 4: Define tools with strict schemas 🔩

Tools should be:

Instead of a tool called do_anything(input: string), make:

  • search_kb(query: string) -> results[]

  • create_ticket(title: string, body: string, priority: enum) -> ticket_id

  • send_email(to: string, subject: string, body: string) -> status OpenAI Function calling guide

If you give the agent a chainsaw, don’t be shocked when it trims a hedge by removing the fence too.

Step 5: Build the controller loop 🔁

Minimum loop:

  1. Start with goal + initial context

  2. Ask model: “Next action?”

  3. If tool call - execute tool

  4. Append observation

  5. Check stop condition

  6. Repeat (with max steps) LangChain “Agents” docs

Add:

Step 6: Add memory carefully 🗃️

Short-term: keep a compact “state summary” updated every step. LangChain “Memory overview”
Long-term: store durable facts (user preferences, org rules, stable docs).

Rule of thumb:

  • if it changes often - keep it short-term

  • if it’s stable - store long-term

  • if it’s sensitive - store minimally (or not at all)

Step 7: Add validation and a “critic” pass 🧪

A cheap, practical pattern:

  • agent generates result

  • validator checks structure and constraints

  • optional critic model reviews for missing steps or policy violations NIST AI RMF 1.0

Not perfect, but it catches a shocking amount of nonsense.

Step 8: Log everything you’ll regret not logging 📜

Log:

Future-you will thank you. Present-you will forget. That’s just life 😵💫

7) Tool calling that doesn’t break your soul 🧰😵

Tool calling is where “How to Build an AI Agent” becomes real software engineering.

Make tools dependable (dependable is good)

Dependable tools are:

Add guardrails at the tool layer, not just prompts

Prompts are polite suggestions. Tool validation is a locked door. OpenAI Structured Outputs

Do:

  • allowlists (which tools can run)

  • input validation

  • rate limits OpenAI Rate limits guide

  • permission checks per user/org

  • “dry-run mode” for risky actions

Design for partial failure

Tools fail. Networks wobble. Auth expires. An agent must:

A quietly effective trick: return structured errors like:

  • type: auth_error

  • type: not_found

  • type: rate_limited
    So the model can respond intelligently instead of panicking.

8) Memory that helps instead of haunting you 👻🗂️

Memory is powerful, but it can also become a junk drawer.

Short-term memory: keep it compact

Use:

  • last N steps

  • a running summary (updated every loop)

  • current plan

  • current constraints (budget, time, policies)

If you dump everything into context, you get:

  • higher cost

  • slower latency

  • more confusion (yes, even then)

Long-term memory: retrieval over “stuffing”

Most “long-term memory” is more like:

  • embeddings

  • vector store

  • retrieval augmented generation (RAG) RAG paper

The agent doesn’t memorize. It retrieves the most relevant snippets at runtime. LlamaIndex “Introduction to RAG”

Practical memory rules

  • Store “preferences” as explicit facts: “User likes bullet summaries and hates emojis” (lol, not here though 😄)

  • Store “decisions” with timestamps or versions (otherwise contradictions pile up)

  • Never store secrets unless you truly have to

And here’s my imperfect metaphor: memory is like a refrigerator. If you never clean it, eventually your sandwich tastes like onions and regret.

9) Planning patterns (from simple to fancy) 🧭✨

Planning is just controlled decomposition. Don’t make it mystical.

Pattern A: Checklist planner ✅

  • Model outputs a list of steps

  • Executes step-by-step

  • Updates checklist status

Great for onboarding. Simple, testable.

Pattern B: ReAct loop (reason + act) 🧠→🧰

  • model decides next tool call

  • observes output

  • repeats ReAct paper

This is the classic agent feel.

Pattern C: Supervisor-worker 👥

This is valuable when tasks are parallelizable, or when you want different “roles” like:

  • researcher

  • coder

  • editor

  • QA checker

Pattern D: Plan-then-execute with replanning 🔄

  • create plan

  • execute

  • if tool results change reality, replan

This prevents the agent from stubbornly following a bad plan. Humans do this too, unless they’re tired, in which case they also follow bad plans.

10) Safety, reliability, and not getting fired 🔐😅

If your agent can take actions, you need safety design. Not “nice to have”. Need. NIST AI RMF 1.0

Hard limits

  • max steps per run

  • max tool calls per minute

  • max spend per session (token budget)

  • restricted tools behind approval

Data handling

  • redact sensitive inputs before logging

  • separate environments (dev vs production)

  • least-privilege tool permissions

Behavioral constraints

  • force the agent to cite internal evidence snippets (not external links, just internal references)

  • require uncertainty flags when confidence is low

  • require “ask clarifying question” if inputs are ambiguous

A reliable agent is not the most confident one. It’s the one that knows when it’s guessing… and says so.

11) Testing and evaluation (the part everyone avoids) 🧪📏

You can’t improve what you can’t measure. Yeah, that line is cheesy, but it’s annoyingly true.

Build a scenario set

Create 30-100 test cases:

Score outcomes

Use metrics like:

  • task success rate

  • time-to-completion

  • tool error recovery rate

  • hallucination rate (claims without evidence)

  • human approval rate (if in supervised mode)

Regression tests for prompts and tools

Any time you change:

  • tool schema

  • system instructions

  • retrieval logic

  • memory format
    Run the suite again.

Agents are sensitive beasts. Like houseplants, but more expensive.

12) Deployment patterns that don’t melt your budget 💸🔥

Start with a single service

Add cost controls early

  • caching retrieval results

  • compressing conversation state with summaries

  • using smaller models for routing and extraction

  • limiting “deep thinking mode” to the hardest steps

Common architecture choice

  • stateless controller + external state store (DB/redis)

  • tool calls are idempotent where possible Stripe “Idempotent requests”

  • queue for long tasks (so you don’t hold a web request open forever)

Also: build a “kill switch”. You won’t need it until you really, really need it 😬

13) Closing notes - the short version on How to Build an AI Agent 🎁🤖

If you remember nothing else, remember this:

An agent isn’t magic. It’s a system that makes good decisions often enough to be valuable… and admits defeat before it causes damage. Quietly comforting, in a way 😌

And yeah, if you build it right, it feels like hiring a tiny digital intern who never sleeps, occasionally panics, and loves paperwork. So, basically an intern.

FAQ

What is an AI agent, in simple terms?

An AI agent is basically a loop that repeats: take input, decide the next step, use a tool, read the result, and repeat until it’s done. The “agentic” part comes from acting and observing, not just chatting. Many agents are just smart automation with tool access, while others behave more like a junior operator that can recover from errors.

When should I build an AI agent instead of just using a prompt?

Build an agent when the work is multi-step, changes based on intermediate results, and needs reliable tool use (APIs, databases, ticketing, code execution). Agents are also useful when you want repeatable outcomes with guardrails and a way to check “done.” If a simple prompt-response works, an agent is usually unnecessary overhead and extra failure modes.

How do I build an AI agent that doesn’t get stuck in loops?

Use hard stop conditions: max steps, max tool calls, and clear completion checks. Add structured tool schemas, timeouts, and retries that won’t retry forever. Log decisions and tool outputs so you can see where it derails. A common safety valve is escalation: if the agent is uncertain or repeats errors, it should ask for help rather than improvise.

What’s the minimum architecture for How to Build an AI Agent?

At minimum you need a controller loop that feeds the model a goal and context, asks for the next action, executes a tool if requested, appends the observation, and repeats. You also need tools with strict input/output shapes and a “done” check. Even a roll-your-own loop can work well if you keep state clean and enforce step limits.

How should I design tool calling so it’s reliable in production?

Keep tools narrow, typed, permissioned, and validated—avoid a generic “do_anything” tool. Prefer strict schemas (like structured outputs/function calling) so the agent can’t hand-wave inputs. Add allowlists, rate limits, and user/org permission checks at the tool layer. Design tools to be safe to rerun when possible, using idempotency patterns.

What’s the best way to add memory without making the agent worse?

Treat memory as two parts: short-term run state (recent steps, current plan, constraints) and long-term retrieval (preferences, stable rules, relevant docs). Keep short-term compact with running summaries, not full transcripts. For long-term memory, retrieval (embeddings + vector store/RAG patterns) usually beats “stuffing” everything into context and confusing the model.

Which planning pattern should I use: checklist, ReAct, or supervisor-worker?

A checklist planner is great when tasks are predictable and you want something easy to test. ReAct-style loops shine when tool results change what you do next. Supervisor-worker patterns (like AutoGen-style role separation) help when tasks can be parallelized or benefit from distinct roles (researcher, coder, QA). Plan-then-execute with replanning is a practical middle ground for avoiding stubborn bad plans.

How do I make an agent safe if it can take real actions?

Use least-privilege permissions and restrict risky tools behind approval or “dry-run” modes. Add budgets and caps: max steps, max spend, and per-minute tool call limits. Redact sensitive data before logging, and separate dev from production environments. Require uncertainty flags or clarifying questions when inputs are ambiguous, instead of letting confidence replace evidence.

How do I test and evaluate an AI agent so it improves over time?

Build a scenario suite with happy paths, edge cases, tool failures, ambiguous requests, and prompt-injection attempts (OWASP-style). Score outcomes like task success, time-to-completion, recovery from tool errors, and claims without evidence. Any time you change tool schemas, prompts, retrieval, or memory formatting, rerun the suite. If you can’t test it, you can’t reliably ship it.

How do I deploy an agent without blowing up latency and costs?

A common pattern is a stateless controller with an external state store (DB/Redis), tool services behind it, and strong logging/monitoring (often OpenTelemetry). Control costs with retrieval caching, compact state summaries, smaller models for routing/extraction, and limiting “deep thinking” to the hardest steps. Use queues for long tasks so you’re not holding web requests open. Always include a kill switch.

References

  1. National Institute of Standards and Technology (NIST) - NIST AI RMF 1.0 (trustworthiness & transparency) - nvlpubs.nist.gov

  2. OpenAI - Structured Outputs - platform.openai.com

  3. OpenAI - Function calling guide - platform.openai.com

  4. OpenAI - Rate limits guide - platform.openai.com

  5. OpenAI - Runs API - platform.openai.com

  6. OpenAI - Assistants function calling - platform.openai.com

  7. LangChain - Agents docs (JavaScript) - docs.langchain.com

  8. LangChain - Tools docs (Python) - docs.langchain.com

  9. LangChain - Memory overview - docs.langchain.com

  10. arXiv - ReAct paper (reason + act) - arxiv.org

  11. arXiv - RAG paper - arxiv.org

  12. Amazon Web Services (AWS) Builders’ Library - Timeouts, retries, and backoff with jitter - aws.amazon.com

  13. OpenTelemetry - Observability primer - opentelemetry.io

  14. Stripe - Idempotent requests - docs.stripe.com

  15. Google Cloud - Retry strategy (backoff + jitter) - docs.cloud.google.com

  16. OWASP - Top 10 for Large Language Model Applications - owasp.org

  17. OWASP - LLM01 Prompt Injection - genai.owasp.org

  18. LlamaIndex - Introduction to RAG - developers.llamaindex.ai

  19. Microsoft - Semantic Kernel - learn.microsoft.com

  20. Microsoft AutoGen - Multi-agent framework (documentation) - microsoft.github.io

  21. CrewAI - Agents concepts - docs.crewai.com

  22. Haystack (deepset) - Retrievers documentation - docs.haystack.deepset.ai

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