AI agent development services for growing businesses
Custom AI agent development for companies that need an agent to act inside their own systems. We define its tools, write it in code on the Claude and OpenAI APIs, test it against a set of your real cases, and keep it passing those tests as models and APIs change.
- Typed tool definitions and schema-checked outputs
- A test set of your real cases run before every release
- A trace of every step the agent took
Why weekend-built agents break in month two
Demos skip the edge cases
A prototype handles the happy path. The forwarded thread, the attachment in the wrong format and the reply to a three-week-old email were never tested.
No one can explain a bad action
When the agent updates the wrong record, there is no trace of what it read, which tool it called or why it chose to.
A model update shifts behavior
The provider releases a new version or retires an old one, and outputs change without anyone rerunning a test.
What custom AI agent development includes
The engineering around the model that keeps an agent predictable.
Tool design
Each action is a narrow, typed function such as find_customer, create_draft_bill or add_crm_note, with validated arguments. The agent cannot reach an endpoint it was not given.
Structured outputs
Responses are constrained to a JSON schema and validated in code. Anything that fails validation is retried once, then handed to a person.
Evaluation suite
A set of real, anonymized cases with expected results, scored on every prompt or model change, so regressions appear before release rather than after.
Injection and data guards
Inbound email and documents are treated as untrusted. Tool access is restricted, PII is masked before it reaches the model where the task allows, and high-impact calls need approval.
Tracing and alerts
Each run stores inputs, model calls, tool calls, latency and token cost. Error spikes, timeouts and unusual spend raise alerts.
Versioned maintenance
Prompts, tools and model versions live in source control. Each month we review failures, add them to the test set and handle provider deprecations.
Agent patterns we have engineered
Order intake agent
Reads customer POs, checks part numbers and drafts the sales order.
RFQ quoting agent
Reads RFQ packages and drafts a quote for an estimator to price.
Bill coding agent
Extracts invoice lines and proposes GL coding from history.
Ticket drafting agent
Drafts support replies grounded in your policy documents.
Legal intake agent
Screens new inquiries and gathers conflict-check details for review.
CRM upkeep agent
Logs calls and proposes deal stage updates from email and transcripts.
Engineering choices that matter after launch
Model-agnostic by design
Model calls go through one internal interface, so moving a task between Claude and OpenAI models, or to a newer version, is a tested change instead of a rewrite.
Rate limits and retries handled
Calls back off and queue when a provider or your CRM rate-limits, so a busy morning delays work instead of losing it.
Your repo, your keys, your traces
Code, prompts, test set and logs live in your accounts, and the README is written so another developer can take it over.
Least privilege by default
The agent gets only the scopes its tools need. A support agent with no refund tool cannot issue a refund, whatever an email tells it.
“He got me 9 sales in my first 7 days of launching our campaign, which exceeded my expectations!”
Common questions
What do AI agent development services include?
Scoping the job, designing the tools the agent can call, writing the agent in code, building a test set from your data, deploying it to your cloud account and maintaining it as models and APIs change.
What is the difference between an agent and a chain of prompts?
A fixed chain runs the same steps every time. An agent picks its next tool based on what it found, which suits work like finding a customer, checking their last order, then drafting a reply. We use fixed chains wherever the steps never vary.
How do you test an AI agent before it touches live data?
First against the test set built from your past cases, then in shadow mode, where it proposes actions on live work without executing them. It goes live when its proposals match your team's decisions at the rate you agreed.
What happens when OpenAI or Anthropic retires the model we use?
Providers announce deprecations in advance. We run the test set on the replacement, adjust prompts where scores drop, and switch only when results match or improve.
Can the agent run on a model we host ourselves?
Yes, where the task allows. Open-weight models can run in your own cloud for data that should not leave it, often with some accuracy loss on harder tasks. We measure that on your test set before recommending it.
Who owns the code and the test data?
You do. The repository, prompts, evaluation set, logs and model API accounts are in your company's name from the start.
Scope your first custom agent
Take the 2-minute AI audit, and we will use your answers to sketch the agent's tools, data and test plan.
- Gather past cases with known outcomes
Old emails, tickets or documents paired with what a person decided, then anonymized. They become both the agent's spec and its test set.
- Define tools and credentials
Every system call is written as a typed function with the narrowest credential that works, such as draft-only access to the accounting API.
- Score, read failures, rescore
We run the test set, read every miss, adjust prompts, tools or plain-code rules, and repeat until results meet the bar your team set.
- Release in shadow mode, then maintain
Live, it proposes actions for a person to approve. Each correction joins the test set, and every later change must pass before it ships.