AI agent development company
From $4,000Custom AI agents that do the work and show their receipts.
We build agents that take real actions inside your systems: resolving tickets, qualifying leads, reconciling invoices. Each one has a narrow job, a short list of tools, a person approving anything risky, and a test set that proves it still works after every change.
By Axit · Updated Sep 23, 2026 · Prices from 15 agencies and 7 cost guides, all linked
Don't take our word for it. Run one. Pick a task, decide how much the agent may do alone, and approve or reject what it wants to do. It runs on open models against a sandbox of fake company data, and plays a recorded example when they're busy.
1 · the task
2 · how much it may do alone
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- tool calls
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- asked a human
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- acted alone
- example
- mode
The short answer
An AI agent development company designs and builds software that uses a language model to decide what to do next and then does it, through tools connected to your systems. Cost guides quote $25,000 to $80,000 for a production agent; agencies that publish prices start around $3,558 for smaller scopes. Running it costs tokens, hosting and the people who review its work.
- Published entry price
- $3,558
- median of 15 agencies
- Production agent, guides
- $25k–80k
- 7 cost guides
- Typical build
- 6–12 wks
- production agent
- Run cost, small agent
- <$100/mo
- in tokens, small model
Definition
What does an AI agent development company do?
It turns a job a person does today into an agent that does it reliably: deciding which step comes next, acting through a small set of tools, and stopping for a human where a mistake would be expensive. Most of the work isn't the model. It's the tools, the integrations, the test cases and the guardrails around it.
What we build
- The agent, with tool calling, memory where it's needed, retries and a cost limit per run.
- Its tools and integrations, each one narrow, with read and write permissions kept apart.
- An evaluation set of real cases, run on every change.
- An approval queue and audit log, so people approve, correct and trace every action.
What you own at the end
- The code, prompts and test set, in your repositories.
- The infrastructure and model accounts, in your cloud and your name.
- A runbook your team can follow when something changes.
- The option to keep us on a retainer, or not.
Honest comparison
Agent, chatbot, workflow or RPA: which do you need?
If you can draw the process as a fixed flowchart, you want a workflow, not an agent. It's cheaper and more predictable. Agents earn their cost when the next step depends on what the input says.
| Type | Takes actions | Order of steps | Cost to run | Use it when |
|---|---|---|---|---|
| Workflow automation | Yes | Fixed | Lowest to run | The steps are the same every time |
| RPA bot | Yes, by clicking screens | Fixed | Licences add up | Old software with no API |
| Chatbot | Rarely | Answers only | Low | People ask questions and need answers |
| AI agent | Yes, through tools | Decides as it goes | Per step, in tokens | The next step depends on what the input says |
Interactive · 5 questions
Build, buy or hire? A five-question decision rule
Plenty of agents should be bought, not built. Helpdesk and CRM vendors ship decent ones for generic jobs. Answer these honestly; if the answer is “buy”, our guide to off-the-shelf customer service agents is a better next step than a call with us.
01Can you describe the job in one sentence that fits any company?
02Does it need to read or write in 3 or more of your systems?
03Does it depend on data or rules only your company has?
04Would a wrong action cost real money or a customer?
05Will it run tens of thousands of times a month?
Recommendation
5 to go
Answer all five for a recommendation.
Sourced · dated Sep 23, 2026
What AI agent development costs in 2026
These ranges reconcile 7 public cost guides with what agencies actually publish. The biggest drivers are the number of systems the agent touches, how clean your data is, and how much it may do without a person.
| Tier | What it does | Build cost | Build time | Monthly run cost |
|---|---|---|---|---|
| Single-task agent | One job, 2 to 4 tools, a human approves the output. Answers from your docs, triages a queue, drafts replies. | $5k to $25k | 2 to 6 weeks | $100 to $1,000/mo |
| Production workflow agent | Acts inside 3 or more systems, with an eval set, audit log and approval queue. | $25k to $80k | 6 to 12 weeks | $500 to $5,000/mo |
| Multi-agent system | Several specialist agents handing work to each other, with shared memory and monitoring. | $80k to $250k | 3 to 5 months | $4,000 to $20,000/mo |
| Enterprise platform | Many teams, compliance reviews, on-prem or private models, internal tooling around the agents. | $250k and up | 5 months and up | $10,000/mo and up |
Our starting price
From $4,000
A single-task agent with its tools, a test set, an approval step and an audit log. Scope sets the final quote: more systems, more autonomy and compliance work move it up the tiers above.
Updated 2026-09-24
Why published prices and cost guides disagree by ten times
Across 15 agencies in our 2026 pricing survey, the median published starting price for an agent or chatbot build is $3,558, and the middle half start between $1,718 and $5,869. Cost guides quote $25,000 to $80,000 for a production agent. Both are right. Guides are written by software firms describing enterprise scope: several integrations, compliance and months of work. Published price lists come from smaller agencies selling a defined agent on a platform. Ask any quote which of the two you are buying.
Agencies' published entry prices (n=15)
Interactive · model prices pulled Sep 23, 2026
What it costs to run an AI agent every month
Build cost gets all the attention, but running cost decides whether an agent pays back. It's mostly model tokens and the people who check its work, and the second is often bigger. Prices below are current list prices from the OpenRouter model list (provider list prices).
Model
Estimated running cost
$935/mo
$0.312 per run
Assumes 3,000 input and 300 output tokens per step at Claude Sonnet 5 list prices ($2 / $10 per million). Add hosting, usually $20 to $300 a month, and any per-seat fees for the tools it connects to.
By department
AI agents we build, by use case
Customer service agents
Read the ticket, look up the order or account, resolve the common cases, and hand the rest to a person with a summary.
A person approves: Refunds, credits and anything sent to a customer.
Sales and SDR agents
Research inbound leads, check the CRM for duplicates, qualify against your criteria, update records and draft the first reply.
A person approves: Every outbound email, at least at first.
Operations and back-office agents
Match invoices to purchase orders, reconcile records, chase missing documents, and flag anything that doesn't add up.
A person approves: Payments and changes to financial records.
Internal knowledge agents
Answer staff questions from your docs, tickets and wikis, with a citation for every claim and a clear 'I don't know'.
A person approves: Nothing needs approval; it only reads. Access follows each person's permissions.
Answering phones? That's a voice agent; see chatbot and voice development.
Anatomy of a build
How we build an agent, in six steps
- 01
Pick one job
A single task with a clear right answer and a person who does it today. Not a general assistant.
- 02
Collect real cases
Fifty to a few hundred real inputs with the outcome a good employee would produce. These become the test set.
- 03
Design tools and gates
The short list of actions the agent may take, which ones need approval, spending limits and stop conditions. Written down before code.
- 04
Build and evaluate
The agent runs against the test set on every change, so a prompt tweak can't quietly break last week's cases.
- 05
Shadow run
It works alongside the person doing the job, proposing instead of acting, until its answers match.
- 06
Widen autonomy slowly
Gates come off one action at a time, only where the logs show the agent has earned it.
AI agent integration
Connecting agents to the tools you already use
An agent is only as useful as what it can reach. We give it narrow tools into your systems, with reads and writes separated and every write logged. Where several agents or assistants need the same access, we expose it once as an MCP server.
CRMs
HubSpot, Salesforce, Pipedrive, Attio: read records, create deals, log activity.
Helpdesks
Zendesk, Intercom, Freshdesk, Help Scout: read tickets, draft or post replies, tag and route.
Workspace
Gmail, Outlook, Google Drive, Notion, Slack: read context, draft messages, file documents.
Money
Stripe, QuickBooks, Xero: look up charges and invoices; refunds and payments always behind approval.
Your own systems
Postgres, internal APIs and admin tools, exposed as narrow tools or an MCP server.
MCP
The Model Context Protocol lets one set of tools serve Claude, ChatGPT and your own agents.
Stack
Frameworks and models: what we use and when
Frameworks are a trade: less code to write, more abstraction to debug. For most single agents, the model providers' SDKs are enough.
| Option | Best for | State | Lock-in |
|---|---|---|---|
| Plain model SDK (Anthropic, OpenAI) | Most single agents. Fewest moving parts, easiest to debug. | You write it | Low; swapping models is a small change |
| Claude Agent SDK / OpenAI Agents SDK | Agents that need handoffs, built-in tool loops and tracing. | Built in | Medium; tied to one provider's conventions |
| LangGraph | Long, branching workflows that must pause, resume and be inspected. | Explicit graph state | Medium; its own abstractions |
| CrewAI | Prototyping role-based multi-agent teams quickly. | Managed for you | Higher; harder to debug in production |
Models change every few months, and so do prices. We keep the model behind one interface and re-run the test set before switching, so moving from one provider to another is a measured decision, not a rewrite.
What we learned in production
Agents fail quietly. So ours check their own work.
SalesClawd, our own product, runs ten specialist agents in parallel. The biggest lesson was that agents are rarely wrong in interesting ways. They're wrong in quiet ways: an edit that looks right and never reached the live page, a claim with no source. So every change is read back from the real system and verified before a person sees it. It costs extra tokens on every run, and it's the reason people trust the approval queue. Client agents get the same habit.
See SalesClawdInteractive checklist
10 questions to ask any AI agent developer (including us)
Tick the ones they answered well. If they can't tell you what it costs to run, or where a human approves, keep looking.
Tick each question the agency answered well.
0/10
Questions
Frequently asked
How much does it cost to build a custom AI agent?+
Public cost guides put a single-task agent at $5,000 to $25,000, a production agent working across several systems at $25,000 to $80,000, and multi-agent systems at $80,000 to $250,000 or more. Agencies that publish price lists start lower: across 15 agencies in our 2026 survey, the median entry price for an agent or chatbot build was $3,558, for smaller scopes. Our own agent builds start from $4,000 for a single-task agent; scope sets the final quote.
How much does it cost to run an AI agent each month?+
Mostly model tokens, hosting and someone reviewing its work. A small agent handling a few thousand runs a month on a small model can cost under $100 in tokens; a busy production agent on a frontier model runs into thousands. Use the estimator on this page with your own volume.
How long does it take to develop an AI agent?+
A single-task agent typically takes 2 to 6 weeks, a production agent across several systems 6 to 12 weeks, and a multi-agent system 3 to 5 months. Most of the time goes into collecting real test cases, integrations and a shadow run, not the model.
What does an AI agent developer do?+
They define the job, give the model a small set of tools to act through, connect those tools to your systems, build a test set from real cases, and add the guardrails: approval steps, spending limits, logs and monitoring.
Is ChatGPT an AI agent?+
The ChatGPT app is mainly a chatbot, but it now includes agent features that can browse, run code and complete tasks. A custom AI agent is different: it acts inside your own systems, with tools and permissions you define.
What is the difference between an AI agent and a chatbot?+
A chatbot answers questions. An agent takes actions: it calls APIs, updates records and sends messages, deciding the next step from what it finds. Many products are a chatbot on the outside with an agent behind it.
Who are the big 4 AI agents?+
There is no official list. People usually mean the agent products from OpenAI, Google, Microsoft and Anthropic, sometimes Salesforce. For a custom build it matters less: the same agent can run on any of their models.
What are the types of AI agents?+
Textbooks list simple reflex, model-based, goal-based, utility-based and learning agents. In business, the useful split is by job: support, sales, operations and knowledge agents, and by how much they may do without a person approving.
Can I build my own AI agent instead of hiring someone?+
Yes, for a simple agent. No-code tools and the model providers' agent SDKs make a prototype a weekend project. Hiring pays off when the agent must work reliably across several systems, handle money or customers, or be tested and maintained.
Should I build a custom agent or buy an agent platform?+
Buy when the job is generic and fits one tool, such as support answers inside your helpdesk. Build when it needs three or more of your systems, your own data or rules, strict approval on risky actions, or high volume where per-seat fees add up.
What are the risks of using AI agents, and how do you control them?+
Wrong actions, made confidently, and quiet failures that look right. They are controlled with a small toolset, human approval on anything that moves money or reaches a customer, a test set run on every change, full logs, and checking results by reading them back.
Which models and frameworks do you use?+
Usually the Anthropic or OpenAI SDKs directly, with Claude or GPT models chosen per task, and LangGraph when a workflow must pause and resume. We keep the model behind one interface so it can be swapped when prices or quality change.
Who owns the code, prompts and data?+
You do. The code, prompts, test set and infrastructure live in your repositories and cloud accounts from the first day.
Bring one job. We'll tell you if it needs an agent.
A 30-minute call with the person who would build it. You leave knowing whether to build, buy or skip it, and roughly what it would cost to build and to run.