Introduction

AI Agent Development

AI Agent Development Services

We design and build AI agents that do work, not just chat: agents that plan multi-step tasks, call your tools and APIs, and write validated records into your systems, with limits and human approval where a mistake would be costly. For AI features inside software you already run, see our AI integration services.

By Obaid Tariq, Founder & Lead Engineer · Last updated 31 October 2026

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Key Facts

AI Agent Development at a Glance

AI agent development with AI Dev Planner: what we build, how a project runs, safeguards, pricing, communication and ownership.
What we build	Tool-calling agents and multi-step workflows that read from and write to your CRM, database, documents and internal tools
How it runs	Use-case audit, then a prototype on your real data, then a production build, then ongoing monitoring
Safeguards	Typed, validated tool calls, step and spend limits, human approval for high-impact actions, and an audit log of every action
Quality and cost	Evaluation against real tasks before release; success rate, latency and model usage costs monitored after
Pricing	No published AI price list: scoped and quoted after discovery
Communication	A dedicated Slack channel and bi-weekly video walkthroughs
Ownership	You own the full IP in the code, prompts, tools and pipelines we build; third-party models stay under their providers' licences

Is ai agent development the right service?

This page: Choose when the job is a multi-step task an agent should complete by calling your tools and APIs, with limits and human approval.

Not quite? Compare

What AI Agent Development Solves

Chatbots That Cannot Act

A chat window can suggest what to do, but staff still copy the answer into three other systems. An agent completes the task through those systems.

Brittle Automation

Rule-based workflows break when inputs vary. Agents handle variation, provided the tools and rules they work within are tightly defined.

Demos That Never Ship

Agent prototypes impress on clean examples, then stall on real data, real permissions and real costs. We build for production from the first prototype.

Actual Deliverables

AI Agents We Build

Tool-Calling Agents

Agents that call your APIs through function calling, with every tool's inputs and outputs defined as typed schemas and validated before anything runs.

Multi-Step Workflow Agents

Agents that break a task into steps, such as research, draft, check and file, with explicit limits on steps, retries and spend.

Back-Office Agents That Write Records

Agents that turn emails, forms and documents into validated records in your database or CRM, with an audit log of every change.

Human-in-the-Loop Approval

Review queues where a person approves high-impact actions, such as sending, paying or deleting, before the agent carries them out.

Permission-Aware Data Access

Agents only read and change what the requesting user is allowed to, enforced at the data layer. For regulated data, see our secure AI development service.

Evaluation and Monitoring

Test sets built from real tasks, plus production monitoring of task success, latency and usage costs, so regressions are caught early.

Side by Side

Chatbot, Workflow Automation or AI Agent?

How an AI agent differs from a chatbot and from fixed workflow automation, and when each is the right choice.
	Chatbot or assistant	Workflow automation	AI agent
What it does	Answers questions and drafts text for a person to use	Runs the same fixed steps every time a trigger fires	Decides which steps and tools a task needs, then carries them out
Acts on your systems	No, a person copies the output	Yes, along a path someone designed in advance	Yes, through tools you expose, within limits you set
Handles variation	In language only	Poorly: unexpected input breaks the flow	Well, if the task and tools are clearly defined
Main risk	Wrong or invented answers	Brittle flows that need constant maintenance	Wrong actions taken automatically
Choose it when	People need help finding or writing information	The process is predictable and rarely changes	Tasks vary but the goal, the tools and the rules are clear

Is an AI Agent the Right Tool?

This is for you if

A repetitive task with variation
The same kind of task arrives often, but each case needs a few judgement calls a fixed workflow cannot make.
The tools already exist
The systems the agent needs, such as your CRM, database or ticketing tool, have APIs or can be given one.
Mistakes can be caught
Actions can be validated, limited or approved by a person before they become expensive.
You want it in production, not a demo
You need evaluation, monitoring and audit logs, not only a prototype that works on sample data.

Not the right fit if

The process is fully predictable
If the steps never change, conventional automation is cheaper and easier to test than an agent.
There is no defined task yet
Agents work best when the goal and the allowed actions are clear. Start by naming the task worth automating.
Every action is irreversible and high-stakes
Where any error is unacceptable and nothing can be reviewed first, keep a person in the loop and use AI for drafting only.

AI Agent Tech Stack

OpenAI Function CallingZod Output ValidationTypeScriptPythonNode.jsPostgreSQL Vector SearchNext.js

How an AI Agent Project Runs

Define the Task, Tools and Limits

Audit

We agree the task the agent should complete, the systems and tools it may use, the actions that need human approval, and what success looks like.

Prototype on Real Tasks

Prototype

We test the agent against real examples from your business in a staging environment, measuring success rate, response time and cost before you commit to a full build.

Production Agent

Build

We connect the agent to your systems behind access controls, with validated tool calls, limits, approval queues and audit logs, shown in bi-weekly video walkthroughs.

Evaluate and Monitor in Production

Monitor

After release we monitor task success, latency and usage costs, and re-run evaluations when your data, tools or model versions change.

Proven Experience

AI Agent Development case studies

Projects that used this capability. Client names and specific data flows are anonymised for confidentiality.

No public case study for this service yet

See our full list of case studies, or ask us for an architecture walk-through on a call.

AI Agent Work Under NDA

How we share evidence

Client projects are usually covered by confidentiality agreements, so we do not name clients or publish their figures here. Ask for an architecture walk-through on a call, where the engineers who did the work explain the decisions they made and why.

How we handle security and delivery Meet the engineering team

Risk Vectors

Common AI Agent Risks

Wrong Actions Taken Automatically

An agent that misreads a task can update the wrong record or send the wrong message, and do it quickly and repeatedly.

Prompt Injection Through Data

Instructions hidden in emails, documents or web pages the agent reads can try to make it misuse its tools.

Runaway Loops and Costs

Without limits, an agent can retry, loop or call expensive tools far more often than the task needs.

Technical Defense

How We Reduce Those Risks

Least-Privilege Tools and Approval

Each agent gets only the tools its task needs, write actions are validated against schemas, and high-impact actions wait for a person to approve them.

Untrusted Input Stays Untrusted

Content the agent reads is treated as data, never as instructions, and tool permissions do not depend on what that content says.

Limits, Logs and Monitoring

Step, retry and spend limits are set per task, every action is logged for audit, and costs are tracked from the prototype onwards.

What You Keep

Ownership, Security and Handover

Code and accounts in your name

We recommend the repository lives in your own GitHub or GitLab organisation from day one, with hosting, domains and third-party services in accounts your company controls. You can see every commit as it happens.

You own the bespoke work

You receive full IP in the source code, database design and documentation we write for you. Open-source and third-party components stay under their own licences, and we list them. The exact ownership wording is set out in each project's contract and our terms of service.

Security built into the build

Secrets stay in server-side configuration rather than client code, access rules are enforced on the server and in the database rather than only in the interface, and production access is limited to the people who need it.

Documented handover, then a warranty

Before handover you receive setup and deployment documentation, environment configuration and a walkthrough, so your own developers or another team can take over. A 60–90 day warranty after launch covers defects in what we delivered; ongoing support is optional and scoped separately.

AI Agent Development: Common Questions

What is the difference between an AI agent and a chatbot?+

A chatbot answers questions or drafts text for a person to act on. An AI agent completes a task itself: it decides which steps are needed, calls tools such as your CRM or database through their APIs, checks the results and carries on until the task is done or it hits a limit you set. That ability to act is why agents need validation, limits and audit logs that a chatbot does not.

What can an AI agent safely do in our systems?+

Whatever you explicitly allow through the tools it is given, and nothing else. We define each tool with typed inputs and outputs, validate every call before it runs, apply the requesting user's permissions at the data layer, and route high-impact actions, such as sending, paying or deleting, to a person for approval.

Do you build autonomous agents or human-in-the-loop agents?+

Both, and most production agents are a mix. Low-risk steps, such as reading records or drafting a reply, can run autonomously; actions that would be costly to get wrong wait for approval. We decide where the line sits with you during the audit, based on the cost of a mistake in each step.

Which models and frameworks do you use for AI agents?+

We choose per use case rather than forcing one provider. Our builds typically use enterprise model APIs with function calling, TypeScript or Python, and schema validation such as Zod for every tool call. If your organisation already standardises on a cloud or model provider, we build within it.

How do you test an AI agent before it goes live?+

We build test sets from real tasks and measure task success, response time and cost against them in a staging environment. Tool calls are validated against typed schemas, and we test failure cases such as missing data, tool errors and instructions hidden in the content the agent reads. After release we monitor the same measures in production.

What does AI agent development cost?+

We do not publish an AI price list, because cost depends on the number of tasks and tools, the systems involved, the approval and security requirements, and ongoing model usage fees. We scope the work in a discovery phase and quote against that scope. A prototype on your real tasks is often the first step, so you can see results before committing to a full build.

Who owns the agent, prompts and tools you build?+

You do. You own the full IP in the source code, prompts, tool definitions, pipelines and documentation we build, kept in repositories and accounts you control. Third-party foundation models remain under their providers' licences, and we document which components are yours and which are licensed.

What do you need from us to start an AI agent project?+

A clear task, the systems it may use, and where a mistake would be costly. In the audit we agree the task, the tools, the actions that need human approval, and what success looks like, then prototype on your real tasks in a staging environment before a full build. Our AI agent development guide at /blog/ai-agent-development covers the background in more depth.

Planning Guides and Free Tools

Useful before you talk to us or anyone else: written for buyers comparing options, not as a sales pitch.

Services Related to AI Agent Development

Projects rarely stop at one discipline. These services are often scoped alongside this one.

AI Integration

Add LLM features to the software you already run.

AI Development

New LLM products, AI agents and custom ML models.

Secure AI Development

Private LLMs and permission-aware RAG.

Python Development

AI, data and FastAPI services in production.

Have a Task an AI Agent Could Take On?

Tell us the task, the systems involved and where time is lost. We will tell you whether an agent is the right tool, what it would need, and what a prototype on your real tasks would involve. We work remotely with businesses worldwide, including the US, UK, Europe and the Gulf.

Book a Free Scoping Call See Case Studies

Not ready to build? Validate Your Idea First