Task agents
An agent that owns one workflow end to end: triaging inbound requests, preparing quotes, reconciling records, or researching leads.
AI agent development that holds up on real data.
Velvy builds AI agents that do a defined job inside your business: they read your data, call your tools and APIs, and hand off to a person when they should. Every agent ships with evals, guardrails, and a log of each step it takes, so you get a working, tested, observable system instead of a demo. We build on the OpenAI and Anthropic APIs in TypeScript or Python.
Spec/services/ai-agent-development
Best-fit engagement
Agent Pilot
3–4 weeksBuilt with
- OpenAI API
- Anthropic API
- TypeScript
- Python
- Node.js
- PostgreSQL
- Supabase
- Vercel
- Cloudflare
Working
Deployed to production
Tested
Automated tests and evals
Observable
Logs, traces, and alerts
What we build
Agents with a job description, not a chat window.
Tool and API integrations
Typed tool definitions that let the agent read and write to your database, CRM, ticketing, or internal APIs, with permissions scoped to what the job needs.
Retrieval over your knowledge
Grounding in your documents, tickets, and product data, so answers point to a source instead of guessing.
Human-in-the-loop review
Approval steps for actions that move money, contact customers, or change records, with a queue your team can work from.
Evaluation suites
A test set built from real cases and scored on every change, so you can see whether a prompt or model swap made things better or worse.
Observability and cost controls
Logs and traces of every run, step, and tool call, with alerts and per-run cost tracking.
How we build it
Scoped, measured, then shipped.
The stack
- OpenAI API
- Anthropic API
- TypeScript
- Python
- Node.js
- PostgreSQL
- Supabase
- Vercel
- Cloudflare
01
Define the job
We pick one workflow, write down what done looks like, and collect real examples, including the messy ones.
02
Build the eval first
Before tuning prompts we build the test set and scoring, so progress is measured rather than eyeballed.
03
Build the agent and its tools
Model choice, tool design, retrieval, and guardrails, tested against the eval set as it grows.
04
Deploy, watch, hand off
A production deploy with logging and alerts, a runbook, and documentation your team can maintain.
How to engage
Start with Agent Pilot.
An Agent Pilot puts one agent into production on your real data. It is the quickest honest answer to whether an agent earns its place in your workflow.
We start with paid discovery, never an unpaid test project, so the plan rests on your real requirements.
Agent Pilot
3–4 weeksOne production agent on your real data: discovery, build, evals, deploy, docs, and handoff.
Also fits
Automation Audit
1 weekNot sure which workflow to start with? An Automation Audit maps the candidates and ranks them first.
Common questions
AI agent development, answered.
Something else on your mind? Ask us directly at hello@velvy.ai, or book the intro call below.
What is an AI agent, in practical terms?
Software that uses a language model to decide which steps to take toward a goal, then takes them by calling tools: querying a database, updating a record, drafting a message. The useful ones have a narrow job, a clear definition of done, and a person in the loop for anything risky.
How do you stop an agent from making things up or taking the wrong action?
Several layers: grounding answers in your data with retrieval, limiting tools to the permissions the job needs, validating outputs against schemas, requiring human approval for consequential actions, and an eval suite that catches regressions before they reach production. No model is perfect, so we design for mistakes to be visible and recoverable.
Which model do you use?
Whichever performs best on your eval set at an acceptable cost and latency. We build on the OpenAI and Anthropic APIs and keep the model behind a thin interface, so it can be swapped when a better option appears.
Can the agent work with our existing systems?
Yes, if they have an API, a database we can reach, or webhooks. Part of discovery is mapping which systems the agent needs, what access it gets, and what it should never touch.
How do you measure whether the agent works?
Against the eval set agreed during discovery: task success, accuracy on the cases that matter, escalation rate, latency, and cost per run. After launch the same signals are visible in the logs, not just at demo time.
What happens after the pilot?
The handoff includes the code, the eval suite, a runbook, and documentation. From there teams run it themselves, add a second agent, or keep us on through fractional engineering.
Get started
Bring the messy problem. Leave with a plan.
Book an intro call. Tell us what you’re trying to build or automate, and we’ll tell you plainly whether we’re a fit and what a first engagement would look like.
Prefer email? hello@velvy.ai