Building an AI Agent Together: A Step-by-Step Guide

An AI agent is a software application powered by a large language model (LLM) that can perform tasks autonomously. It plans actions, uses external tools, evaluates results, and corrects mistakes when necessary. Unlike a traditional chatbot, an AI agent doesn't just answer questions. It completes the entire task.

There has never been a better time to start building AI agents. Powerful language models are accessible to everyone, development tools have matured, and tutorials, documentation, and real-world examples are everywhere. Businesses are rapidly adopting AI automation, while the demand for professionals who can build AI agents continues to outpace the available talent. The only thing standing between you and your first AI agent is getting started.

Choose one repetitive task that you'd like to automate. Build a simple AI agent using the platform or framework of your choice, and launch it today. Your first version doesn't have to be perfect. What matters is gaining practical experience and understanding how AI agents work. From there, you can improve your prompts, add tools, introduce memory, configure proxies, and scale your solution. Every new agent you build will be better than the last. A month from now, you'll wonder how you ever managed those tasks manually.

In this guide, you'll learn how to build an AI agent from scratch. We'll explain what an AI agent is, how it works, which tools you'll need, and how to turn an idea into a fully functional solution in eight practical steps. At the end of the article, you'll also find a practical checklist to help you determine whether your AI agent is ready for production.

What Is an AI Agent

Let's start with the main question that trips up almost every beginner: how is an AI agent different from a regular chatbot?

Chatbot vs. Agent

A classic chatbot works on a "question – answer" pattern. You type a message, the model generates text, and the dialogue ends. The bot doesn't remember yesterday, can't open a website, send an email, or write data to a spreadsheet. It's confined to the boundaries of the conversation.

An AI agent is built differently. Given a task, it:

1. Breaks it down into steps.

2. Decides which tools are needed for each step.

3. Takes action – searches the web, calls APIs, reads files.

4. Evaluates the result and adjusts the plan if necessary.

5. Returns a finished outcome, not just text.

A simple example. You ask a chatbot: "Find me three hotels in Istanbul under $100." The bot will give you general recommendations from its training data, possibly outdated. An agent, on the other hand, will open booking sites, gather current prices, compare options, and return a table with real offers.

How an Agent Works

Inside every agent runs a cycle known as the agentic loop: "think → act → observe the result → think again." The language model acts as the "brain," deciding at each iteration what to do next. The loop repeats until the task is complete or the attempt limit is reached.

What It's Made Of

A typical AI agent has five basic components:

1. Language model (LLM) – the core that understands the task and makes decisions.
2. System prompt – the instructions defining the agent's role, rules, and behavioral boundaries.
3. Tools – functions through which the agent acts: web search, API requests, working with files and databases.
4. Memory – short-term (the current session's context) and long-term (a knowledge base, history, facts about the user).
5. Orchestration – the logic tying everything together: the execution loop, error handling, limits.

Takeaway: an agent isn't a "smarter chatbot" but a system where the model manages tools and memory to achieve a goal. It's exactly this combination of "brain + hands + memory" that makes artificial intelligence genuinely useful at work.

Where AI Agents Are Used

To keep things concrete, let's look at areas where agents are already delivering measurable value.

1. Customer support. The agent reads an inquiry, finds the answer in a knowledge base, checks the order status via API, and replies to the customer. Complex cases get handed off to a human operator along with a brief summary of the conversation. A typical result – 40–70% of routine inquiries resolved without human involvement.
2. Sales. Lead qualification: the agent asks clarifying questions, evaluates the prospect's potential, enters the data into the CRM, and books a meeting with a manager. Not a single inbound request gets lost, even at night.
3. Marketing. Brand mention monitoring, competitor analysis, price collection from marketplaces, ad campaign reporting. Work that used to take an analyst days, an agent handles in minutes.
4. Analytics. The agent connects to a database, writes SQL queries from plain-language requests ("show me revenue by region for the quarter"), builds charts, and explains anomalies.
5. Programming. Tools like Claude Code let you delegate writing functions, hunting bugs, refactoring, and writing tests to an agent. The developer sets the task – the agent delivers working code.
6. Content creation. The agent researches a topic, gathers sources, prepares an article draft, fact-checks, and structures the piece for SEO requirements. The editor is left with the final review.
7. Business process management. Processing incoming invoices, sorting email, syncing data between services, deadline reminders – classic automation, but with a flexibility rigid scripts never had.

Takeaway: agents shine where there are repetitive tasks with a clear outcome and a lot of routine. That's exactly where you should start.

What You'll Need Before Building

Before you create an AI agent, let's assemble the builder's kit. We'll go through each element.

Language Model

The main options today:

For your first agent, any of the top models accessed via API will do. Open models are worth considering later – when you develop data privacy requirements or want to cut costs at high volumes.

API

An API is how your code communicates with the model. You send a request (prompt, conversation history, list of tools) and receive a response. You'll need an API key, issued in the provider's dashboard. The key is essentially a password: never publish it in open code or share it with third parties.

Tools

Tools are functions the model can call. Modern models support function calling: you describe a function (name, parameters, what it does), and the model decides on its own when to invoke it. A typical starter set:

• web search;
• HTTP requests to external APIs;
• reading and writing files;
• database access;
• sending messages (email, messengers).

Memory

Without memory, an agent starts from a blank slate every time. Ways to organize it:

1. Context window – the history of the current conversation, the agent's "RAM."
2. Vector database (Pinecone, Qdrant, Chroma, and others) – stores knowledge as embeddings and enables search by meaning rather than exact match. This is the foundation of RAG (Retrieval-Augmented Generation).
3. Regular database or files – for structured data: customer profiles, order history, settings.

Prompts

A prompt is an instruction for the model. The system prompt sets the agent's "character" and rules; user prompts deliver specific tasks. Prompt quality directly determines the quality of the agent's work, so we'll return to writing them separately.

External Services and Integrations

An agent rarely lives in a vacuum. It will most likely need access to a CRM, spreadsheets, a calendar, messengers. There are two paths:

1. No-code platforms (n8n, Make, Zapier) – you assemble the agent from ready-made blocks, with almost no code required.
2. Developer frameworks (LangChain, LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK) – more flexibility, but you'll need basic Python or JavaScript skills.

A separate layer of infrastructure is the network. If your agent works heavily with external websites (scraping, price monitoring, checking search results), you'll need proxies for AI – we'll cover them in detail below, because many scenarios simply don't work without them.

Takeaway: the minimum kit is a model via API, a couple of tools, and a well-thought-out system prompt. Everything else gets added as your tasks grow.

Building an AI Agent Step by Step

Time for practice. Let's walk through how to create an AI agent using a running example: we'll build a research agent that monitors competitor prices and sends a report. The example is hypothetical, but every step applies to any task.

Step 1. Define the Task

The most underrated stage. Before writing a single line of code, answer four questions in writing:

1. What should the agent do? Not "help with marketing," but specifically: "once a day, collect prices for 50 products from three competitor websites and send a table showing the changes."
2. What counts as success? For example: data collected for all products, error rate below 5%, report delivered by 9 a.m.
3. What data and access does it need? The list of websites, the list of products, where to send the report.
4. Where are the boundaries? What the agent must not do: change prices in your own store, message customers, exceed a set API budget.

A rule for beginners: one task – one agent. A universal "agent for everything" almost always performs poorly at everything at once.

Step 2. Choose the Model

Selection criteria:

1. Task complexity. For classifying emails, a cheap, fast model is enough. For multi-step analysis with reasoning, go with a flagship.
2. Cost. Pricing is per token (roughly ¾ of an English word). An agent running a loop of 10–20 steps burns through tokens quickly, so price differences between models add up over time.
3. Speed. If the agent replies to customers in live chat, a 30-second delay is unacceptable. For an overnight report – not a problem.
4. Tool use. Check how reliably the model calls functions: for agents, this is critical.

A practical strategy: start with a strong model, get the logic right, then try moving some steps to a cheaper model and compare the quality.

Step 3. Set Up the Instructions (System Prompt)

The system prompt is the agent's job description. A good prompt includes:

1. Role: "You are a pricing analyst for an electronics online store."
2. Task: exactly what to do and in what format to deliver the result.
3. Rules: "If a product's price can't be found, mark it as 'no data' – never invent values."
4. Constraints: "No more than three retry attempts per website."
5. Examples: one or two samples of a correct result work wonders – the model understands format far better from an example than from a description.

Write the prompt in short, unambiguous sentences. "Try to be careful" doesn't work; "before sending the report, verify that the number of rows equals the number of products in the list" does.

Step 4. Connect the Tools

Our research agent will need:

1. A page-fetching tool – retrieves HTML from a URL.
2. A data-extraction tool – pulls the product name and price from the page.
3. A writing tool – saves the results to a spreadsheet or database.
4. A report-sending tool – email or a messenger notification.

Every tool needs a clear description: the model chooses what to call based precisely on it. Bad: "a function for websites." Good: "fetches the HTML of a page at the given URL; returns the page text or an error message."

This is also where the network question comes up. Websites don't like automated requests: dozens of hits from a single IP address trigger defenses – CAPTCHAs, rate limits, bans. If your agent does web scraping, build proxies into the architecture from day one, not after the first ban. More on that in a dedicated section below.

Step 5. Add Memory

Our agent needs two kinds of memory:

1. Working memory: the product and website lists, results of the current run. Stored in a regular table or a JSON file.
2. Historical memory: prices from previous days, for calculating trends. Any database will do, even SQLite.

For agents that talk to people, a third layer gets added – memory about the user: preferences, past interactions, context. This is where a vector database and RAG come in handy: before answering, the agent searches the knowledge base for relevant fragments and relies on them rather than on the model's "general impressions." This sharply reduces hallucinations – fabricated facts.

Step 6. Handle Errors

Errors will happen. Always. The only question is whether a single error brings down the whole process. What to plan for:

1. Retries. A site didn't respond – wait and try again, maximum 2–3 times.
2. Timeouts. Every external call must have a time limit.
3. Iteration limits. The agent must not loop forever: set a maximum number of steps (say, 25), after which it stops and reports the problem.
4. Graceful degradation instead of crashing. Failed to collect 3 products out of 50 – the report still goes out, with the problematic items flagged.
5. Logging. Record every step the agent takes: which tool was called, with what parameters, what came back. Without logs, debugging turns into guesswork.
6. Human-in-the-loop. Irreversible actions – sending money, deleting data, publishing – must require human confirmation.

Step 7. Test

Test in three passes:

1. The happy path. All data is in place, all sites respond. The agent succeeded? Great, that's only the beginning.

2. Problem scenarios. A site is down, a price comes in an unusual format, a product vanished from the catalog, a page returned a CAPTCHA. This is exactly where agents usually break.

3. Edge cases. An empty product list, duplicates, extremely long names, an unexpected currency.

Build a set of 15–20 test cases and run it after every change to the prompt or code. Keep in mind: models are non-deterministic – the same request can produce different responses. So evaluate stability across a series of runs, not a single one.

Step 8. Improve the Results

Launch isn't the finish line – it's the start of an improvement cycle:

1. Analyze the logs. Where does the agent err most often or waste extra steps? Those are your growth points.
2. Refine the prompt. Turn every recurring mistake into a rule: the agent mixed up currencies – add the instruction "convert all prices to USD at the specified rate."
3. Split the task. If one agent can't handle it all, divide the roles: one collects data, a second analyzes, a third writes the report. Such multi-agent setups are often more stable than a single "generalist."
4. Count the money. Track token spend. Often 80% of costs come from 20% of the steps, which can be simplified or moved to a cheaper model.
5. Gather feedback. If people use the agent, give them a "bad answer" button and review every such case.

Takeaway: building an agent is 20% "AI magic" and 80% ordinary engineering: clear task definition, error handling, testing, and iteration.

Why an AI Agent Needs Proxies

Here's the section beginners skip – and then lose days getting unblocked. Let's figure out what proxies have to do with it.

What a Proxy Is

A proxy server is an intermediary between your agent and the internet. Requests go not directly from your IP address but through an intermediate server, so the website sees the proxy's IP, not yours. The main types:

Why Agents Struggle Without Proxies

An AI agent working with the web doesn't behave like a human: it fires dozens of requests per minute, moves through pages methodically, and runs around the clock. Anti-bot systems spot this kind of activity from a single IP very quickly. The result – CAPTCHAs, rate limiting, temporary and permanent bans.

The tasks proxies solve when paired with artificial intelligence:

1. Data collection for the agent. Price monitoring, catalog parsing, gathering reviews and listings – all of it requires a high volume of requests that need to be spread across different IPs.
2. Bypassing geographic restrictions. Some content and prices vary by country. Residential proxies with geo-targeting let the agent "view" a site from the right country or city – critical for monitoring local search results and regional prices.
3. SEO rank tracking. Search engines personalize results; for the agent to see honest rankings by region, requests must come from IPs in those regions.
4. Managing multiple accounts. If the agent handles accounts across different services, each needs its own stable IP – otherwise the service will link them together.
5. Collecting training datasets. For teams preparing data to fine-tune models, proxies help gather large volumes of information without bans.

Why You Need Different IP Addresses

The logic is simple: a hundred requests from one address looks like a suspicious bot; one or two requests from a hundred different addresses looks like ordinary traffic. IP rotation (automatically switching the address on a timer or with every request) spreads the load and makes the agent's activity indistinguishable from that of many regular users. For reliable AI automation, this isn't a "trick" – it's standard engineering practice, the same way monitoring systems at major retailers and SEO platforms are built.

How to Choose Proxies for an AI Agent

A practical tip: match the proxy type to the task. For scraping ordinary sites, datacenter proxies are often enough; for marketplaces and heavily protected services, go with residential proxies; for social networks – mobile.

Among the services meeting these requirements, Astro stands out – a platform offering residential, mobile, and datacenter proxies in over 100 countries with a pool of more than 50 million IP addresses. For agentic workflows, the convenient parts are IP rotation via API or on a timer, HTTP(S) and SOCKS5 support, country- and city-level targeting, and pay-as-you-go pricing – you can start with a small amount of traffic and scale as you grow. There’s also a free $3 trial credit, so you can test the proxies with your specific use case before making a purchase. To get the trial, simply contact our support team.

Takeaway:  if your agent goes online more than a couple of times an hour, proxies are as much a part of its infrastructure as the API key and the database. Build them in from the start.

The Most Common Beginner Mistakes

This list is battle-tested – check yourself against every item.

1. A task that's too broad. An "agent for business automation" is doomed. Start with one narrow process and expand after you succeed.
2. Blind trust in model output. Models hallucinate – they confidently state fabricated facts. Anything the agent presents as fact must be grounded in data from tools or a knowledge base, and critical conclusions must be verified.
3. No limits. An agent without iteration and budget caps can rack up an impressive API bill overnight. Set ceilings: maximum steps, maximum tokens, maximum daily spend.
4. A vague system prompt. "Be a helpful assistant" is not an instruction. The more specific the rules and examples, the more predictable the agent.
5. Ignoring network errors. The very first unreachable site takes down the whole process because the author didn't plan for retries and timeouts.
6. Scraping without proxies and pauses. The agent gets banned after a hundred requests and work grinds to a halt. IP rotation and sensible intervals between requests solve the problem.
7. Storing API keys in the code. A key that lands in a public repository gets found within minutes – and someone else spends your money. Use environment variables.
8. Full autonomy from day one. Giving an agent the right to take irreversible actions without human confirmation is a path to painful incidents. Expand autonomy gradually, as trust builds.
9. Testing only the happy path. The real world is made of messy data and downed websites. Test exactly those.
10. No logs. When the agent fails (and it will), without a record of its steps you'll never figure out where.

How to Make an AI Agent Genuinely Useful

A few principles that separate a toy agent from a working one.

1. Do the math. A useful agent saves more than it costs. Estimate how many human hours per month the agent replaces versus what the tokens, proxies, and infrastructure cost. If the balance is negative – simplify.
2. Feed the agent good data. A model connected to an up-to-date knowledge base via RAG is vastly more useful than one answering "off the top of its head." Invest time in structuring your documents, guidelines, and FAQs.
3. Design for failure, not success. A good agent isn't one that never errs, but one whose errors don't turn into disasters: it notices them, reports them, and keeps working.
4. Define human handoff points. The best deployments aren't "AI instead of people" but "AI for the routine, people for the hard stuff." Spell out exactly when the agent must call in a human.
5. Start in draft mode. For the first while, let the agent prepare results and a human approve them. When the correction rate drops to a minimum – switch on autonomous mode.
6. Track your metrics. Task success rate, average time, cost per operation, share of escalations to a human. What isn't measured doesn't improve.
7. Maintain regularly. Websites change their markup, APIs change their formats, models change behavior after updates. Budget an hour or two a week for upkeep.

Conclusion

An AI agent is a language model given tools, memory, and the right to act. Today, a person without deep technical knowledge can build their first agent: models are available via API, no-code platforms lower the entry barrier, and frameworks cover developers' needs.

Where a beginner should start:

1. Pick one narrow task with a clear outcome – ideally a boring routine you do by hand every week.

2. Assemble a minimal version: model + system prompt + one or two tools.

3. Run it through tests, including the "bad" scenarios.

4. Launch it in draft mode, gather feedback, improve.

5. Only then add memory, new tools, and autonomy.

And don't forget the infrastructure: spending limits, logs, error handling, and – for web tasks – quality proxies with IP rotation. Boring things that separate a working product from a pretty demo.

AI Agent Pre-Launch Checklist

Before you launch your AI agent, make sure everything is ready. This checklist will help you verify that it's properly configured and prepared for real-world tasks.