AI agents that remember, reason, and learn — in production, inside the software you already run.
Most AI projects stall between the demo and the deploy. We build agentic systems that carry state across sessions, plan and verify their own work, and improve from feedback — wired into your Rails, Python, or Node application with the evals, guardrails, and observability production demands.
No commitment · 15 minutes · Fixed-scope estimate
A demo is not a system
Getting a model to do something impressive once takes an afternoon. Getting it to do the right thing ten thousand times, on your data, inside your app, with nobody babysitting it, is engineering. That gap is where most AI initiatives die.
Agentic systems, built for production
End to end — from the first workflow worth automating to an agent your team trusts to run unattended.
Agent Systems
Single agents and multi-agent workflows that plan, call tools, and verify their own output before acting. Built on Claude and the Anthropic SDK, with model routing where it saves money.
Memory & Retrieval
Persistent state across sessions and retrieval over your documents and data, using Postgres and pgvector or the stores you already run. Context that gets sharper the longer the agent operates.
Learning Loops
Evals, human corrections, and outcome data feed back into prompts, retrieval, and routing, so the system improves on a schedule instead of drifting.
Workflow Automation
Multi-step business processes — intake, triage, document handling, reconciliation, follow-up — with humans in the loop exactly where the risk warrants it.
Integration Into Your Stack
Wired into your Rails, Python, or Node application, your auth, and your deployment pipeline. No parallel system to maintain.
Evals, Guardrails & Observability
Test suites for agent behavior, hard limits on what an agent may do, cost and latency budgets, and traces you can actually debug.
What "remember, reason, and learn" actually means
Remember
State that survives the session. The agent knows what happened last time, what your users prefer, and what has already been tried — stored in your database, not in a black box.
Reason
It plans before it acts. Work is broken into steps, the right tools are called, results are checked against the requirement, and a person is brought in when the agent isn't sure.
Learn
It gets better with evidence. Every correction, eval result, and outcome becomes data that tunes prompts, retrieval, and routing — measured, not hoped for.
From one workflow to a system you trust
Incremental and measured at every step, so you always know whether it's working.
Discovery call
Fifteen minutes. What's stuck between demo and production, which data and systems are involved, and whether an agent is even the right tool. If it isn't, we'll say so.
Agent readiness audit
A written assessment of your codebase, data, and workflows; the first automation candidates ranked by value and risk; and a fixed-scope proposal for the first sprint.
Sprint one
Three to four weeks. One production-grade agent or workflow shipped into your app with its eval suite, guardrails, and monitoring — not a prototype.
Measure and expand
Evals and outcome data decide what comes next. Each sprint adds capability against a scoreboard, so nobody has to guess whether the AI is earning its keep.
Hand off or retain
Your team owns the code, the prompts, and the evals. Keep us on retainer for ongoing AI engineering, or take it from here.
Twenty-five years of production, applied to AI
Layer 3 is led by Shawn Cunningham, a senior engineer who has built and operated production systems across fintech, legal tech, healthcare, and hospitality since 2004 — and who now builds agentic systems on Claude every day.
Frequently asked questions
A chatbot answers questions. An agent does work: it holds state, plans multi-step tasks, calls your systems, checks its results, and hands off to a person when it should. We build the second kind, and we build it inside your existing application rather than as a separate tool.
Primarily Claude through the Anthropic SDK, with Claude Code for development. We route to other models when cost or capability warrants it, and we work within your constraints — cloud provider, data residency, or vendor agreements.
That's the point. Agents that live outside your app become one more system to maintain. We integrate with your auth, your data, and your deployment pipeline. On a legacy version? We upgrade Rails, Python, and Node apps too, and can do both in the same engagement.
Three layers: hard limits on what an agent may do, verification before any consequential action, and human checkpoints where the risk warrants it. All of it is covered by an eval suite that runs on every change, so regressions show up before your users see them.
Three to four weeks for the first production agent or workflow. Every engagement is scoped from the audit, so you get a fixed, milestone-based estimate before we write code — no open-ended hourly billing.
It stays in your systems. We design for your data-handling requirements from the start — HIPAA and SOC 2 environments included — and we document exactly what goes to which model provider and why.
Yes. Everything we build — code, prompts, evals, documentation — is yours. We'd rather you keep us because we're useful than because you're stuck.
Ready to get an AI project past the demo?
Book a free 15-minute call. We'll talk through what's stuck, which data and systems are involved, and whether a fixed-scope sprint makes sense.