Selected Work Services Case Studies Pricing Contact Blog Revora — Free Revenue Audit Free ADA Scan Our Tools ↗
Home› Services› AI Agents
Software That Does the Work

AI Agent
Development

An agent is not a chatbot with a better prompt. It is software that reads context, decides what to do, and then actually does it across your real systems. That takes guardrails, evaluation, and architecture most AI projects skip. We build the version that survives production.

Get Free Consultation → All Services
The Distinction

Answering Questions vs
Getting Things Done

Most of what gets sold as an AI agent is a chatbot with a longer prompt. The real difference is consequence. A chatbot that misunderstands someone produces a bad sentence. An agent that misunderstands someone produces a wrong refund, a corrupted record, or an email to the wrong customer.

That single shift changes the entire engineering discipline. You now need permission scoping, human checkpoints on irreversible steps, logging detailed enough to reconstruct any decision, and an evaluation suite that proves correctness before launch rather than after complaints. None of that shows up in a demo. All of it shows up three weeks into production.

If you are still deciding whether you need an agent at all, our practical guide to AI app development walks through the honest filter.

What We Build

Agent Patterns That
Earn Their Keep

Action-Taking Agents
Agents wired to your real systems through scoped tools: update a CRM record, issue a refund, book a slot, reconcile two ledgers, escalate an exception to a human.
Retrieval-Grounded Agents
RAG done properly, where answer quality is driven by chunking, indexing and ranking rather than prompt wishful thinking. Bad retrieval produces confident, wrong answers.
Multi-Step Workflow Agents
Agents that carry a task across several systems and several decisions, with state, retries, and a clean audit trail at every hop.
Human-in-the-Loop Agents
Agents that handle the routine majority and hand off cleanly, with full context, the moment a case needs judgment. The handoff is designed, not an afterthought.
Evaluation Harnesses
A repeatable test suite of real cases with known-correct outcomes, run against every change, so quality is measured rather than eyeballed.
Cost & Latency Monitoring
Per-task inference cost, latency, and output-quality tracking in production, because agent behavior and user input both drift over time.
Why MyMind Studio

The Boring Parts
Are the Whole Job

Evaluation Before Launch

We will not ship an agent whose correctness nobody can measure. Every build gets a test set of real cases with known answers, run on every change. This is the step that separates a working system from a good demo.

Guardrails on Anything Irreversible

Permission scoping, confidence thresholds, and mandatory human confirmation on actions that cannot be undone. We design the failure path before the happy path.

Data Audit First

We look at where your data lives and how clean it is before proposing a model or a framework. A partner who names a model before asking about your data has not done this before.

You Own Everything

Your codebase, your API keys, your infrastructure, your prompts and evaluation sets. Full ownership, in writing, before work starts. No dependency dressed up as a partnership.

Technologies

Modern Stack.
Boring Where It Counts.

Anthropic ClaudeOpenAI GPTModel Context ProtocolLangGraphVercel AI SDKPythonFastAPINode.jsTypeScriptPostgres & pgvectorPineconeRedisTemporalObservability & tracing
Our Process

How We Work

01

Discovery & Data Audit

What task, for whom, with what data, and what does correct look like. This stage kills bad agent ideas before they cost anything, and it should.

02

Feasibility Prototype

A narrow throwaway build against real data to check whether accuracy is even in the right range, before anyone commits to a full engagement.

03

Build With Guardrails

Tools, permissions, retrieval, state and escalation paths built together, alongside the evaluation suite rather than after it.

04

Launch & Monitor

Production rollout with logging, cost tracking, and output-quality monitoring, plus a defined process for what happens when the agent gets something wrong.

FAQ

Questions Worth
Asking

What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent takes actions. A chatbot can tell a customer their order policy; an agent can look up the actual order, issue the refund, update the CRM, and email the confirmation. That difference is why agents need permission scoping, audit logging, and human checkpoints that a chatbot never required, because a wrong answer becomes a wrong action.
How do you stop an agent from doing something harmful?
Layered guardrails. Permission scoping so the agent can only reach the systems it genuinely needs, human-in-the-loop confirmation on anything irreversible such as payments or deletions, full logging so every decision can be reconstructed after the fact, and confidence thresholds that escalate to a person instead of guessing. We design these before the agent gets access to anything real.
How do you know the agent actually works?
An evaluation framework built alongside it: a set of real test cases with known-correct outcomes that runs against every meaningful change. This is the step most agent projects skip, and it is the single biggest predictor of whether a system that demos well survives contact with real users. Without it you are shipping on vibes.
What does an AI agent build cost to run, not just to build?
Agents call models repeatedly per task, so inference is a real operating cost that scales with usage, not a one-time build fee. A low-traffic internal agent may cost very little monthly; one handling thousands of multi-step tasks daily is a different conversation. We model this with you before launch rather than letting the first invoice be the surprise.
Do we need our own model, or can you use GPT or Claude?
Almost every business should use an existing frontier model through an API rather than training their own. Training from scratch is enormously expensive and rarely justified. The real engineering is in the orchestration around the model: retrieval, tool definitions, guardrails, and evaluation.
How long does an AI agent project take?
A narrow, well-scoped internal agent with clean data can go from discovery to production in a matter of weeks. Agents that take consequential actions across multiple systems take longer, because the guardrail and evaluation work is genuinely more involved than the happy path. Data readiness drives the timeline more than the agent logic does.
Ready to Start?

Let's Talk About
Your Project

Book a free 30-minute consultation. No commitment, no sales pitch — just a real conversation about what you need and whether we are the right fit.

Get Free Consultation → View Pricing