AI Agent Development
Custom AI agents that read your data, use your tools and hand off to people — built with guardrails, approval steps, evals and monitoring.
Built with
Agents that do a job, not demos that answer a prompt
An AI agent is a language model that can take actions: look things up, call your systems, decide the next step and stop when it's done. We build agents around one well-defined job, give them only the tools that job needs, and put a person in the loop wherever an action is hard to undo.
Why AI agents are replacing AI toolsInbox & Lead Agents
Read inbound email and forms, answer from your knowledge base, qualify the lead and write it to your CRM.
Research & Briefing Agents
Gather from the sources you trust and deliver a focused brief or report on a schedule.
Monitoring & Alerting Agents
Watch ad spend, inventory, reviews, pipelines or on-chain activity and flag what changed and why it matters.
Customer-Facing Assistants
Chat and voice agents that answer from your policies, book appointments and hand off to staff with context.
Operations Agents
Agents that propose changes in your systems — budgets, statuses, records — and execute them after approval.
Copilots Inside Your Product
An agent embedded in your app that works over your users' own data, with permissions enforced per user.
For teams that want work done, not another chat window
Most businesses that ask us about AI agents have already used ChatGPT or Claude and seen what a model can do. The gap is between a model that answers questions and a system that reliably does part of someone's job: checks the CRM, reads the attachment, books the slot, updates the record, and tells a person when it isn't sure. Closing that gap is engineering work — permissions, tool design, evaluation and monitoring — and that's what an agent development engagement is.
We build agents for three kinds of clients. Operators who want a recurring task taken off their team: lead intake, inbox triage, research briefs, reporting, monitoring. Product companies that want an agent or copilot inside their own software, working over their customers' data. And businesses that have a pilot or prototype agent that impressed everyone in a demo and then misbehaved on real inputs.
We run agents in our own products, so the patterns are ones we live with. Marketing OS, our ad-operations platform, runs an agent that reviews campaign performance, proposes budget and status changes, checks them against budget and business rules, and holds anything high-risk for a person to approve before it touches a live ad account. The AI Receptionist is a voice agent that answers calls, books into a calendar and transfers to a human when it should. The assistant on this website answers questions about our services and passes qualified leads to our inbox.
The common thread is that the agent is the smallest part of the system. Around it sit the tools it's allowed to call (each one validated and logged), the data it's allowed to see, a record of every decision it made, evaluation suites that catch regressions when a prompt or model changes, and a clear rule for when it must stop and ask. We're model-agnostic — Claude, GPT and open-weight models — and design so you can change providers without rewriting the agent.
A good fit if
- A task is repeated daily or weekly, follows rules you could write down, and still needs some reading or judgment.
- The data the agent needs lives in systems with an API — CRM, calendar, email, database, ad platforms.
- You can name who approves the agent's riskier actions.
- You have an agent prototype that works in a demo but not on real inputs.
- You want to own the code and run it in your own cloud accounts.
Built for teams stuck between options
The signals we look for, the painful realities we hear, and the way we typically resolve them.
Teams buried in inbound
Pain
Leads, support requests and partner emails pile up in shared inboxes, and the reply depends on who has time.
Solution
An agent that reads each message, answers what it can from your knowledge base, qualifies and routes the rest, and logs it all in your CRM.
Operators watching dashboards
Pain
Someone checks ad accounts, inventory or pipeline numbers every morning to catch the one thing that went wrong overnight.
Solution
A monitoring agent that checks on a schedule, explains what changed, and proposes the fix for a person to approve.
SaaS products adding an agent
Pain
Customers want an assistant that can act inside your product, and a chat box bolted onto the side won't do it.
Solution
An in-product agent that calls your own APIs as the signed-in user, so it can only do what that user could.
Stalled agent pilots
Pain
A prototype loops, calls the wrong tool or makes things up as soon as it meets messy real-world data.
Solution
We narrow the job, redesign the tools, add evals built from real cases and put approvals in front of risky actions.
From one well-defined job to an agent in production
Agents fail when the job is vague. Every engagement starts by pinning down exactly what the agent is responsible for and how we'll know it's doing it well.
- 011–2 weeks
Discovery & job definition
We map the task, the systems involved and the decisions a person makes today, then write the agent's job description: inputs, allowed tools, outputs, and when it must hand off. You get a fixed-fee estimate you own whether or not we build it.
- 022–4 weeks
Proof of concept
A working agent on your real data and tools, with an evaluation set built from real past cases. This is where we find out whether the job is agent-shaped before you commit to a full build.
- 036–12 weeks
Production build
Guardrails, approval queues, permissions, audit logs, monitoring and cost controls, plus the admin views your team needs to supervise the agent. It launches supervised and earns autonomy.
- 042-week sprints
Tuning in sprints
Real usage shows where the agent hesitates or errs. We fold those cases into the eval set, adjust tools and prompts, and extend its scope as the numbers support it.
These are typical ranges for a single agent, not promises. Systems with several cooperating agents are scoped in discovery.
Built for ownership, scalability, and the long haul
An agent you can supervise is an agent you can actually deploy.
Guardrails before autonomy
Agents start by proposing and earn the right to act. High-risk actions always wait for a person.
Every decision visible
Each run is logged with its inputs, tool calls and reasoning, so you can see why the agent did what it did.
Measured, not vibes
Evaluation suites built from your real cases catch regressions before a prompt or model change ships.
Opinionated, but not married to a stack
We default to a tight set of tools we trust in production, and pick the right one for each layer.
Models
01Agent frameworks
02Retrieval
03Backend
04Evaluation & monitoring
05Deployment
06Selected engagements
A few projects where we've shipped this kind of work — problem, stack, and outcome at a glance.
Guides on AI Agent Development
AI Agents Are Replacing AI Tools: What to Know in 2026
Read the guideAI Agent Development in Tampa: A Practical Guide for Local Businesses
Read the guideAI Agents for Business in Tampa, Florida: Where They Deliver ROI
Read the guideBuilding an AI Sales Copilot That Actually Understands Your Lead Pipeline
Read the guideQuestions before we start building?
What teams typically ask before kicking off an engagement.
Still unsure what you need?
Book a 30-minute technical discovery call. We'll help you clarify scope and the right next step.
What is an AI agent, and how is it different from a chatbot?
A chatbot answers questions. An AI agent can also take actions: it decides which tool to use, calls your systems — CRM, calendar, email, database — reads the results and continues until the job is done or it needs a person. That ability to act is why agents need permissions, approval steps and logging that a chatbot doesn't.
What kinds of tasks are AI agents good at today?
Tasks that are repeated often, follow rules you could write down, and still need some reading or judgment: triaging an inbox, qualifying leads, researching and summarizing, monitoring metrics and flagging changes, booking appointments, and drafting updates for a person to approve. They're weaker at open-ended work with no clear finish line or no way to check the result.
How do you stop an agent from doing something it shouldn't?
By limiting what it can do in the first place. Each agent gets only the tools its job needs, each tool validates its inputs, and actions that are costly or hard to undo go to an approval queue instead of executing. Every run is logged, and evaluation suites built from real cases catch regressions before changes ship.
Which AI models do you build agents with?
Usually Anthropic's Claude or OpenAI's models for reasoning and tool use, with smaller or open-weight models where cost or data residency matters. We choose per task and keep the agent's tools and evaluations independent of the model, so switching providers doesn't mean a rewrite.
How long does it take to build an AI agent?
Discovery takes one to two weeks. A proof of concept on your real data usually takes two to four weeks, and a production agent with guardrails, approvals and monitoring typically ships in six to twelve weeks, then improves in two-week sprints.
What does AI agent development cost?
It depends on the job, the systems the agent connects to and how much supervision tooling it needs, so we don't quote off a phone call. A paid discovery produces a fixed-fee estimate you own whether or not we build it, and running costs for model usage are estimated alongside it.
We already built an agent prototype. Can you take it to production?
Yes, and it's a common starting point. We review what the prototype does, build an evaluation set from real inputs, and usually narrow its job, redesign its tools and add approvals before widening its scope. Sometimes the right answer is a simpler workflow with one AI step, and we'll say so.
Do you build AI agents for companies outside Florida and California?
Yes. We meet clients in person in Tampa Bay and Greater Los Angeles and build agents remotely for businesses across the US. Discovery and supervision tooling work the same either way.
Working in person in Tampa Bay and Los Angeles
AI Agent Development in Tampa
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AI Agent Development in Los Angeles
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Agents applied to lead follow-up, documents and reporting.
Business Automation in Los Angeles
Automation and agents for Los Angeles operations.
Ready to build?
No pressure. We'll help you clarify scope, budget, and the best technical path forward.