AI Agents in Software Development: A Practical Guide

What is an AI agent?

An AI agent is a program that uses a large language model (LLM) to pursue a goal by choosing actions, calling tools, observing the results and repeating until it is done. Four parts make an agent: a model that reasons, tools it can call, a loop that keeps it going, and memory or context that carries state between steps.

The easiest way to understand the term is to compare it with the two things people confuse it with.

That last row matters. Once a model can run commands and call APIs, its mistakes stop being text you can ignore and become changes you must check.

How do developers use AI coding agents?

Developers use coding agents to take a described task, explore the codebase, edit several files, run the tests and iterate until they pass, then hand back a diff for review. This is a step beyond inline completion, where you accept or reject a suggestion as you type.

Tools differ in packaging, not in the core idea. Claude Code runs in your terminal against your repository. Cursor is an editor built around AI chat and agent modes. GitHub Copilot has an agent mode inside supported editors and can work on assigned tasks and open pull requests. Each reads code, proposes or makes edits and can run commands you approve. If you search for “best AI coding agents 2026”, expect a list that keeps changing; judge tools by capability and fit, not by the ranking.

What agents are good at

• Well-specified, verifiable tasks: add a field and migrate it, write tests for an existing function, convert callbacks to coroutines, fix a bug with a failing test.
• Boilerplate and glue: DTOs, mappers, form screens, API clients, repetitive refactors across many files.
• Exploring unfamiliar code: “where is the session token refreshed?” is a good question for an agent.
• First drafts: documentation, commit messages, test scaffolding.

What agents are bad at

• Vague goals: “make onboarding better” produces confident, unfocused changes.
• Architecture and trade-offs: they will happily pile more code onto a bad design.
• Subtle correctness: concurrency, money handling, edge cases in your own business rules.
• Knowing what they do not know: they can invent an API that does not exist and call it with total confidence.

How do you work well with a coding agent?

Treat the agent like a fast, eager junior colleague: give it a precise task, a way to check its own work, and a reviewer (you) who reads everything. The developers who get the most from agents are usually the ones who could have written the code themselves.

1. Write a clear spec. State the goal, the files or modules involved, constraints (“do not change the public API”) and what “done” means.
2. Keep tasks small. One behaviour or one bug per task. Small diffs are easier to review and easier to throw away.
3. Use tests as guardrails. Ask for a failing test first, then the fix. A test suite lets the agent verify itself and lets you verify the agent.
4. Review every diff. Read it as if a stranger wrote it. Check for unrequested changes, deleted tests and made-up APIs.
5. Never paste secrets. No API keys, signing keys, production database dumps or customer data in prompts or in files the agent can read. Keep them out of the repository entirely.
6. Understand what you ship. If you cannot explain a change line by line, do not merge it. Someone will ask you about it at 2am or in an interview.

This is also why AI-assisted development is taught alongside fundamentals in our courses rather than instead of them. See the course tracks if you want to learn the workflow with a mentor.

How do you build an AI agent into your own software?

You build an agent by giving a model a set of tools, running it in a loop that executes the tool calls it requests, and managing context, safety and cost around that loop. The loop itself is small; most of the engineering is in the tools and the guardrails.

The agent loop and tool calling

With tool (function) calling, you describe each tool with a name, a description and a JSON schema for its arguments. The model does not run anything itself. It replies with a request such as “call get_balance with this account id”, your code runs it, and you send the result back. Here is a generic sketch; exact client calls differ between providers, so check your SDK’s documentation.

def run_agent(goal, tools, llm, max_steps=8):
    messages = [{"role": "user", "content": goal}]
    for step in range(max_steps):
        reply = llm.chat(messages, tools=tools.schemas())
        messages.append(reply.as_message())
        if not reply.tool_calls:          # model is finished
            return reply.text
        for call in reply.tool_calls:
            if not tools.is_allowed(call):    # guardrail
                result = "Denied: not permitted"
            else:
                result = tools.run(call.name, call.args)
            messages.append(tool_result(call.id, result))
    return "Stopped: step limit reached"

Notice the three safety features that are easy to skip: a step limit so it cannot loop forever, an allow-list check before any tool runs, and a clear fallback when the limit is hit.

Retrieval, guardrails and evaluation

• Retrieval: instead of stuffing everything into the prompt, search your documents or database and give the model only the relevant passages. It reduces hallucination and cost.
• Guardrails: validate tool arguments, restrict what each tool can do, require human approval for irreversible actions such as sending money or deleting data, and log every call.
• Evaluation: build a fixed set of realistic test tasks with expected outcomes and re-run it whenever you change the prompt, model or tools. Without this, you are guessing.
• Cost and latency: every loop iteration is another model call. Cap steps, keep context short, use smaller models for simple steps and cache what you can. Users on slow mobile networks will not wait through eight round trips.

What is the Model Context Protocol (MCP)?

The Model Context Protocol is an open standard for connecting AI applications to external tools and data through one common interface. Instead of writing a custom integration for every model and every tool, you expose a tool once as an MCP server and any MCP-compatible client can use it.

In practice, an MCP server advertises capabilities such as tools (actions the model can request), resources (data it can read) and prompts (reusable templates). The client, which might be a coding agent or your own app, connects, discovers what is available and passes it to the model. Many coding agents can connect to MCP servers for things like databases, issue trackers and documentation. The specification and guides are at modelcontextprotocol.io.

Two cautions. An MCP server runs code with whatever access you give it, so only install servers you trust. And every connected tool is more surface for prompt injection, covered below.

What could AI agents do for African businesses?

Agents are a good fit wherever work is repetitive, text-heavy and spread across systems, which describes a lot of small and mid-sized business operations in Kenya and across Africa. These are possibilities worth prototyping, not claims about existing deployments.

• Customer support on WhatsApp: an agent that answers common questions in English or Swahili, looks up an order or booking through an API, and hands over to a human with the conversation summarised when it is unsure.
• M-Pesa reconciliation: an agent that matches payment notifications (for example from the Daraja API) against invoices or orders, flags mismatches and drafts a follow-up, leaving a person to approve any adjustment.
• Document processing: extracting fields from receipts, delivery notes, KRA-related paperwork or loan forms into structured records, with low-confidence fields routed for human checking.

The pattern is the same each time: narrow tools, a human in the loop for anything involving money, and an evaluation set built from real examples. Design for patchy connectivity too, with queues and retries, because your users’ phones will go offline.

What are the risks of AI agents?

The main risks are hallucination, prompt injection, excessive permissions and data privacy. All four get worse as you give an agent more autonomy.

• Hallucination: the model states false things or invents functions and facts fluently. Mitigate with retrieval, tests, citations to source and human review.
• Prompt injection: text the agent reads, such as a web page, an email or a customer message, can contain instructions that try to hijack it. Treat all retrieved content as untrusted data, never as commands, and do not let one agent hold both untrusted input and powerful tools without checks.
• Permissions: give each agent the least privilege it needs. A support agent should read orders, not issue refunds without approval.
• Data privacy: in Kenya, the Data Protection Act 2019 sets rules for processing personal data, including purpose limitation, security and restrictions on transferring data outside the country. Sending customer data to a third-party model is processing. Minimise what you send, redact identifiers where possible, check your provider’s data terms, and get proper legal advice for your situation. I am a developer, not a lawyer.

Will AI agents replace software developers?

Not in the sense of removing the need for developers, but they are changing the job in a real way. Producing a first draft of code is getting cheap; deciding what to build, checking that it is correct, and taking responsibility for it are not.

Honest caveat: nobody can predict exactly how hiring will shift, and entry-level work, which was often boilerplate, is the part most affected. That is a reason to aim higher, not to stop learning. The strongest junior developers can explain why a solution works, not just show that it runs.

• Fundamentals matter more. You cannot review what you do not understand. See why data structures and algorithms matter.
• System design matters more. Agents write components; someone must decide how they fit, fail and scale. Read system design for developers.
• Judgement matters more. Knowing when the agent is wrong, when to throw its work away and when a problem needs a person.

What skills should you learn to work with AI agents?

Learn the fundamentals first, then add agent-specific skills on top. A practical order:

1. One language and platform well, such as Kotlin for Android or Swift for iOS, so you can read and judge generated code.
2. Data structures and algorithms, enough to spot an O(n squared) loop where a hash map would do.
3. Testing and debugging, because tests are your guardrails.
4. HTTP, JSON APIs and databases, since tools are mostly APIs.
5. Prompting and context management: writing specs, giving examples, keeping context focused.
6. Building a small agent with tool calling, an eval set and a step limit; then wire up one MCP server.
7. Security and privacy basics, including prompt injection and data protection law.

For the wider picture, see what to learn as a software developer in 2026. For an official reference on tool use, the Anthropic documentation is a good place to start.

Frequently asked questions

Kevin Lagat

Founder and lead instructor at Bridgetree Learn. Former Senior iOS Engineer at Safaricom, where he worked on the M-PESA app. He has shipped production apps for the UK's NHS, a Saudi digital bank and US SaaS companies, and trained Saudi Digital Academy cohorts through Coding Dojo. LinkedIn · GitHub