AI agent development: agents that use your tools, with limits you set

AI agent development is building software in which a language model decides the next step and calls tools, such as your APIs, database or internal systems, to complete a task. StackEndBox builds these agents for companies and founders who want one running in production, with defined permissions, step limits, logging and human approval for risky actions, instead of a demo that only works on a good day.

This page is about agents specifically. For the wider work of putting AI into software, such as retrieval over your documents, AI features inside a product and AI steps in a workflow, see our broader AI Systems & Agents page. An agent is the right tool for fewer jobs than the current enthusiasm suggests, so we start by checking whether you need one at all.

Who this is for

• Operations and support teams with multi-step work that depends on what is found along the way, such as investigating a case across several systems.
• Product teams adding an assistant to their software that can take actions through the product’s own API, not just answer questions.
• Founders building an agent-based product that needs tools, permissions and reliable behaviour in production.

When this is the right fit

• The task needs several steps whose order depends on what the agent finds, so a fixed workflow cannot be drawn in advance.
• The agent’s work can be checked: you can say what a good result looks like and test it against real examples.
• Mistakes are visible and reversible, or a person approves the action before it takes effect.
• The tools the agent needs already exist as APIs, or can be exposed as APIs, with permissions you can narrow.

What we build

• Tool-using agents

  Agents that call your APIs, databases and internal tools to complete a defined task, with each tool exposed only as far as the task needs.

• Permissions and boundaries

  Read-only versus write access, spending and step limits, and a list of actions the agent may never take on its own.

• Human approval steps

  Review and approval stages for anything irreversible or customer-facing, with the evidence shown beside the proposed action.

• Evaluation harness

  Test cases built from your real examples, including awkward ones, re-run whenever the prompt, model or tools change.

• Logging and traces

  A record of inputs, outputs, tool calls and decisions, so a bad result can be traced and a good one can be repeated.

• Agents inside your product or operations

  Interfaces where people hand work to the agent, review what it proposes and correct it, in the tools they already use.

What you get

• A working agent deployed alongside your product or tools, scoped to the task agreed in writing.
• Written permission and approval rules, enforced in the system rather than left to the prompt.
• A test set built from your real examples, with the results before launch.
• Logs of inputs, outputs and tool calls that someone on your team can read.
• Documentation of which model provider and data sources are used, and exactly what is sent to the provider.

Build, buy or hybrid?

An agent is the most flexible option and the hardest to test. The question is how much freedom the task really needs.

• Plain workflow automation

  If you can write the rule down, ordinary code or a workflow tool is cheaper to run, predictable and easier to test. Most processes stop here.

• A workflow with one AI step

  When the input is messy but the output can be checked, such as classifying an email or extracting fields from a document, one bounded model step inside a fixed workflow is usually safer than an agent.

• An agent

  Appropriate when the task needs several steps in an order you cannot know in advance, you can measure whether it did the job, and its tools and permissions can be limited. Add logging and approval on anything irreversible.

• An off-the-shelf assistant

  A helpdesk add-on or a general assistant may already cover a standard task. Configure it first if your process and data are standard.

When not to hire us. We are probably not the right choice if you want an agent to make high-stakes decisions with no human review, if you need a guaranteed accuracy figure, if nobody can say what a correct result looks like, or if an existing tool already does the job and only needs configuring.

What affects the cost of AI agent development

We do not publish prices, because cost follows scope and risk. These are the drivers that move it most.

• How much freedom the agent has

  A read-only agent that prepares a summary is a smaller project than one that changes records in several systems. Every action it can take needs permissions, limits and tests.

• Number and quality of tools

  Each system the agent uses is an integration to build and test. Clear, well-documented APIs make for a smaller job than tools with poor or missing ones.

• Evaluation

  Building a test set from your real cases, and re-running it whenever something changes, is part of the work. A task with a clear definition of correct is quicker to evaluate than one experts disagree on.

• Approval and audit requirements

  Human approval steps, role-based access and audit trails add design and testing work.

• State of your data

  If the records the agent relies on are scattered, duplicated or out of date, preparing them can be a large share of the work.

• Model usage and volume

  Managed model APIs charge by usage, paid to the provider. More requests, longer tasks and more steps per task raise running cost as well as build cost.

• Monitoring after launch

  Behaviour can drift as inputs, data and models change, so monitoring and adjustment continue after the first release and are agreed separately.

Questions to ask any AI agent development company

Use these with any provider, including us. They are questions to ask, not claims about our results.

1. Do I need an agent, or would a workflow or a single AI step do the job? Will you tell me if the answer is no?
2. What can the agent do without asking, and what always waits for a person? Where is that rule enforced?
3. How will you test it, using which of my real examples, and what happens when a test fails?
4. What are the step, time and cost limits for one run, and what happens when a run hits them?
5. Can I read the logs of what the agent did, and who gets alerted when something goes wrong?
6. Which model provider is used, what data is sent to it and can sensitive fields be excluded or redacted?
7. Who owns the code, prompts, test set and accounts, and can another team take them over?
8. Who re-tests the agent when the model or prompts change, and how is that work agreed?

How we work

We begin with the task and the outcome, then check whether a workflow or a single AI step would do before proposing an agent. If an agent is justified, we design its tools, permissions, step limits and approval points, build the evaluation set from your real examples, and test critical flows and edge cases before launch. After launch we monitor behaviour and adjust as inputs, data and models change.

What we need from you to start

• The task in plain words: what triggers it, what a person does today, which systems they open and what the result should be.
• Real past cases with the correct outcomes, including the awkward ones, so we can test against reality.
• Access to, or a description of, the APIs, databases and tools the agent should use, and which of them it may change.
• Your rules on which data may be sent to an outside model provider.
• A named person who can review the agent’s work during development and decide where approval is required.

Technology

• TypeScript
• Python
• Node.js
• PostgreSQL
• Supabase
• Anthropic

Frequently asked questions

What does an AI agent development company do?

It designs, builds and runs software in which a language model works toward a goal using tools. At StackEndBox that means choosing the tools and data the agent may use, setting its permissions and limits, testing it against your real cases, adding human approval where a mistake would matter, and monitoring it after launch.

How is an AI agent different from a chatbot or a workflow automation?

A chatbot mainly answers questions. A workflow follows steps you defined in advance. An agent chooses its own next step and calls tools to reach a goal, which makes it more flexible and harder to predict and test. Many jobs need only a workflow or one AI step, and we will say so.

When do I need an agent rather than a simpler automation?

When the task needs several steps in an order you cannot know in advance, you can measure whether the result is right, and the tools and permissions can be limited. If you can write the rule down, use plain automation. Our guide to AI agents versus deterministic automation walks through the decision.

How do you stop an agent taking a wrong or risky action?

By design, not by prompt alone: narrow tool permissions, step and cost limits, human approval before irreversible or customer-facing actions, and logs so mistakes are visible. We cannot promise it will never act wrongly, which is why risky actions wait for a person.

How do you test an AI agent?

We build a test set from your real cases, including awkward ones, and run the agent against it before launch and again whenever the prompt, model or tools change. After launch we monitor behaviour, because it can change over time.

Can an agent work with the tools we already use?

Usually, where those tools offer APIs. We assess each tool during scoping, decide what the agent may read and what it may change, and tell you where a tool’s limits make a requirement impractical.

Which AI models do you use?

We build on managed model provider APIs, and our stack includes Anthropic’s models. The provider is chosen with you, and we document what data is sent to it. Self-hosted models are an option when data must stay in your environment, with more operational work.

How much does it cost to build an AI agent?

It depends on scope and risk, so we do not quote without them. The drivers are how much freedom the agent has, the number and quality of its tools, evaluation, approval and audit needs, your data, model usage and monitoring, listed under “What affects the cost of AI agent development”.

What is the difference between this page and AI Systems & Agents?

This page covers agents that use tools to complete multi-step tasks. The AI Systems & Agents page covers the wider work, including retrieval over your documents, AI features inside a product and AI steps in workflows. Many projects use both, and we help you choose the simplest design that works.

Who owns what you build?

Ownership of the code and deliverables is agreed in the written scope before work begins. Model providers and open-source components remain under their own terms.

How do we start?

Book a call or email us a description of the task. We reply with questions about data, risk and what a correct result looks like, then propose a first scoped piece of work.

Related services

• AI Systems & Agents: for the broader AI work around agents: retrieval over your data, AI features in a product and AI steps in workflows.
• API Integrations & Business Automation: to build and harden the APIs and integrations an agent depends on, or when plain automation is enough.
• Custom Software, Web Apps, CRM & Internal Platforms: when the agent belongs inside an internal platform, CRM or customer application.
• AI Automation Services: when the process is fixed and only some steps need AI, a workflow is simpler than an agent.

Decision guides

• AI Agents vs Deterministic Automation: When to Use Each: A practical decision tree for choosing between rule-based automation, an AI step inside a workflow and an AI agent, covering cost, latency and failure modes.
• Where AI Automation Actually Saves Businesses Time, and Where It Doesn't: StackEndBox guide to AI automation: which tasks suit it, human-in-the-loop design, evaluation, running costs, a worked example, checklist and pilot plan.