AI agent development company in New York
Custom AI agents built around how your business actually works. Not demos: software that does real work every day.
An AI agent isn’t a chatbot you switch on or a SaaS tool you subscribe to. A useful one knows your business, works inside the tools you already use, follows your process, and leaves a record of what it did. Getting there takes some careful building, and that’s the part I do.
The gap between a slick demo and an agent that saves your team real hours every week is mostly unglamorous work: predictable workflows, skills that can be tested, logs you can read, and guardrails for the odd cases. I don’t skip that part.
We start by looking at how your team works today and where the hours go. Then I design the agent, build its skills, connect it to your tools, and put it to work. You’ll see what it’s doing the whole way. No black boxes.
How the pieces fit
Choosing the model
Each job gets the model that suits it. Hard reasoning goes to a top model like Claude Opus. High-volume sorting goes to faster, cheaper models through OpenRouter. I won’t pay top-model prices for work a small model handles fine.
Every agent has a rulebook: a plain-English file that sets out its personality, its limits, which tools it may use, and when it has to hand things to a person. It’s version-controlled like any other code.
Around the model sits a simple, predictable layer of code that decides what the agent is allowed to do at each step. Models are clever but not always consistent. That layer is what makes the agent dependable.
Skills do the work
A skill is one small job the agent can do, written as code I can test. Give it the same input and you get the same result, so it can be checked, versioned and audited.
async def qualify_lead(context: LeadContext) -> LeadScore:
"""
Analyze inbound lead and score for fit.
Returns score + routing decision.
"""
analysis = await llm.structured_output(
prompt = QUALIFY_PROMPT,
context = context,
schema = LeadScore
)
if analysis.fit_score > 0.8:
await crm.create_opportunity(analysis)
await slack.notify("#sales", analysis)
return analysisCommon skills: pulling data out of documents, sorting things into categories, calling an API, reading and writing a database, sending notifications, drafting documents, and asking a human for help.
Your tools, not new ones
Your agent connects to the tools you already pay for, through their normal APIs. Nothing gets ripped out and replaced.
If it has an API, I can probably connect to it. If it doesn’t, there’s usually a way around that too.
You can always see what it’s doing
Every agent logs each model call, each tool it uses, and each decision it makes. You get alerts when it does something unexpected, and a simple dashboard showing how much it’s done, what went wrong, and what it cost.
Guardrails come standard: it checks what comes in, filters sensitive data out of what goes out, limits how fast it can act, and asks a person when it isn’t sure. A good agent knows when it’s out of its depth.
Agents are software, and software needs looking after. That’s part of what you get, not an extra.
Watch one work
An example lead-qualification agent handling a new inquiry, step by step. The company and people are made up.
lead_qualify_agent.enrich_company()…score_lead() with enriched context…crm.create_opportunity()…slack.notify()…email.draft()…What is this workflow costing you?
Adjust the sliders to estimate your current annual spend on a single manual workflow.
* A rough illustration that assumes an agent takes over about 78% of the cost. Your numbers will be different. Ask me and we’ll work them out properly.
What you get
- A discovery session where we map your workflow together
- The agent’s design: which model, which tools, and how it decides
- A rulebook that spells out who the agent is, its limits, and when it asks for help
- Tested skills for each job the agent does
- Connections to your existing tools (CRM, email, Slack, databases, APIs)
- Logging and alerts so you can see what it’s doing
- Guardrails, plus a human sign-off for anything high-stakes
- Testing on real scenarios before it touches live work
- A plan for updating it as your business and the models change
Got a workflow in mind? In a 30-minute discovery call we walk through it together, and I tell you honestly whether an agent fits.
Book a discovery call