Agentic AI · Product and engineering · Executive leadership
Product, technology and AI leadership for financial services.
Lean Product Group puts agentic AI to work inside regulated product and engineering organizations: agents that complete work rather than suggest it, under processes a bank, a lender or a regulator can stand behind. We build the operating model, run it, and build the servant leaders who excel.
The thesis
The model is not the product. The operating model is.
Agentic AI is here: the current generation of agents takes an intent, does the work and returns a finished result. Superintelligence, SI, is coming behind it. That is not a copilot. It changes the risk a company carries, because a system that completes work can complete it wrong at scale, and it changes who is accountable when it does.
Most AI programs fail at exactly that point. A demo that worked is handed to an organization with no process, no evidence and no one holding the ship decision. The agents are not the problem. The missing operating model is.
So we start from the beginning and reengineer the organization. Acceptance criteria a human approves before any agent writes code. Tests written to fail first, then locked. Agents that build, and separate agents on a different model that audit them. Processes that software enforces and only your people can publish. Every release carries its own evidence, which matters when your board, your sponsor bank or your regulator asks how it was built. Security, data and leading engineering practices built in from the start and non-negotiable, so the agentic engineering approach is safe and effective at scale.
We run this model every day on production financial software. The method is published and the courses on this website teach it end to end. What we can do is install it in your organization and lead it, before the systems are smarter than the people supervising them. The work is the gap between AI and SI.
Practices
Three ways we work with you
Executive leadership for all organizations
The hard part of a company is leadership: deciding what agents may and may not do, redesigning roles around a small human core that orchestrates them, restructuring processes while training team members when the schedule is screaming, and telling the board the truth about what changed. We take the seat, as advisors or product and technology leadership, to guide organizations to a better way of operating.
Operating cadence, decision rights, org design, and coaching for the product and engineering leaders who inherit it.
Agentic AI engineering for product and engineering teams
Multi-agent delivery from intake to production. Product and compliance agents co-author the acceptance criteria, QA agents write the failing tests, engineer agents turn them green, and independent auditors on a different model check security, scope and the diff. Twenty-two agents with memory, guards that deny instead of warn, and a knowledge graph that learns from every story.
Greenfield when you are starting a product. Brownfield when the agents must first learn the codebase you already run.
Agentic AI in regulated financial stacks
Agents are only useful where they can reach: the core, the ledger, the processor, the loan management system, the sponsor-bank reporting. We design the layer that lets agents act on real systems under real controls, API-first and contract-tested, with an audit trail of everything they touched, and we build the products that run on it.
Payments, lending and banking are home turf: PayFac infrastructure, card-network rules, embedded finance, consumer lending.
The method
From idea to production, with a human deciding at every step that matters.
Agentic AI engineering is more than code generation. Agents do the work at every phase: the business case, the breakdown into customer value, a working version you can click before anyone commits to building it, and the build itself. Your people decide what moves forward.
- 01IdeaA sentence from anyone in the business. Not a ticket, not a spec.
- 02InitiativeAgents turn the idea into an initiative with the business case and ROI attached. The product lead reviews and reworks it.Product lead moves it forward
- 03EpicsCut into distinct chunks of customer value, each with outcomes measured while the work is in flight, not after.Product lead moves it forward
- 04Working mockA usable version in a temporary environment: real tests, API-first, every standard applied. Acceptance criteria are vetted by using the feature.Team vets the AC by using it
- 05Iron PipelineEngineers take it through nine stages and three gates, starting from the mock or from scratch. Every merge deploys; nothing is released until you say so.Three typed gates, H1 · H2 · H3
- 06ProductionDeployed dark behind a feature toggle, blue/green, API-versioned, with the evidence trail attached. Release is a decision, not a deployment: turn it on when the business is ready, off in seconds if it isn't.Released on your call, rolled back in seconds
Inside the Iron Pipeline · the gold stages belong to your people
- S0Intake
- S1Acceptance criteria
- H1You approve the AC
- S2Red: failing tests
- H2You lock the tests
- S3Green: code
- S4Verify & audit
- S5–S7Dev, AC verify, staging
- S8Record & learn
- H3You release it
ROI before a line of code
Every initiative carries its business case from the first draft. If the value can't be stated, it doesn't get an epic.
Acceptance criteria you can click
The working mock is real software in a real environment. The team vets what it will accept by using it, then the engineers build to that.
Tests before code, proven to fail
A QA agent writes failing tests from the acceptance criteria. Once your team acknowledges them they are hash-locked; the engineer agents' only job is to turn them green.
Nobody grades their own homework
Security, diff and acceptance auditors always run on a different model from the engineer that wrote the code. A dispatch guard denies it otherwise.
Compliance is a stage, not a review
A compliance agent co-authors every acceptance criterion and signs the exact version. Card-network, PCI and lending rules load as the domain experts' reference.
It gets better every week
The agents learn their roles, and what they know about your product and business compounds with every piece of work. Your backlog becomes a knowledge base instead of a to-do list.
Who we work with
Companies where "done" being wrong is a regulator's letter.
CEOs and boards of banks, lenders and payments companies who need AI to show up in the P&L, not the slide deck. Product and technology executives who inherited an AI mandate and a legacy stack in the same quarter. Founders building a regulated product who want the delivery system before the headcount.
The firm is led by Hanson Hodges, a chief product and technology officer who has run product and engineering for fintechs through unicorn scale, built PayFac infrastructure for sponsor banks, and runs the agentic operating model in production today.
Results from the work →If your AI program needs an operating model and an executive to own it, let's talk.
Three sentences is enough: where you are, what's in the way, and when you need it solved. We reply within one business day with a straight answer on fit.