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MNM Technology

How It Works

A delivery agency designed around governed AI agents.

Traditional consultancies create capacity by adding people. Standalone coding tools provide suggestions without owning the result. We combine principal level engineering, a client specific context layer, and managed agents to turn defined Java work into reviewed production output.

The delivery model

Ground truth before generated code.

Before agents make meaningful changes, we give them verified context about the relevant repositories, modules, dependencies, interfaces, architecture, build paths, standards, tests, cloud environment, and delivery constraints. That context is exposed through MCP so the agents operate from the estate rather than isolated prompts.

The MCP context layer architecture A coding agent queries an MCP context layer, which exposes the estate as four structured tools: module graph, dependency tree, test coverage, and framework versions. The layer reads from the estate and delegates deterministic computation to production services, which return exact values to the agent. Coding agent retrieves & orchestrates asks MCP CONTEXT LAYER runs inside your VPC · read only by default · every call audited Module graph what exists Dependency tree what breaks Test coverage what is provable Framework versions what is stale Your estate source, build files, test reports · read only Production services all deterministic computation exact values, never inferred
An agent that computes its own answers is an agent that confidently reports wrong ones. The layer is what makes the difference checkable.

How an engagement runs

  • Ground the agents. We map the estate and the delivery rules before any change is made.
  • Execute bounded workstreams. Agents analyze, implement, refactor, test, and document across scoped lanes, in parallel when the engagement permits.
  • A human finishes the work. The principal architect owns requirements interpretation, architecture, security judgment, code review, risk, acceptance criteria, and release governance.
  • Evidence with every change. Review, tests, and documentation, produced while the work happens rather than reconstructed afterward.
  • Outcomes before activity. Progress is measured through accepted deliverables, not agent activity.

Why the model exists

Delivery is the product. Agents are the leverage.

Clients do not engage us to experiment with AI. They engage us to deliver software. We use agents where they create speed and parallelism, while the principal architect retains responsibility for decisions, quality, risk, and production readiness.

Where the leverage actually is

  • Agents assist. Repository and dependency analysis, implementation, refactoring, test generation, documentation, and upgrade preparation.
  • The principal owns. Discovery, architecture, technical priorities, security decisions, code review, stakeholder communication, and production approval.
  • Grounded in real data. The context layer lets agents and automated tooling operate on your actual dependency graph instead of inferring one.
  • Every MCP implementation built fresh from public documentation. Architectural knowledge is general skill; specific implementations are not. Nothing carries between clients.

Tooling

OpenRewrite  ·  AWS Transform  ·  Amazon Q Developer  ·  Moderne  ·  Claude Code  ·  MCP  ·  Repository scale dependency analysis

Source access & security

Answered before you have to ask it.

Every conversation about pointing agents at proprietary source reaches the same questions, so here is the posture in full before you ask.

Boundary

The MCP server runs inside your VPC. No source code leaves your boundary.

Scope

Read only tool scopes by default. Write paths are gated behind explicit approval, per tool.

Audit

Full audit logging of every tool invocation, retained on your infrastructure under your retention policy.

Access model

IAM scoped access mirroring existing repository permissions rather than bypassing them. The client retains its existing access controls, approvals, and production governance: if a person cannot read a repository, neither can the agent acting for them.

Data handling

No training on client data. Model provider terms documented in the statement of work rather than referenced.

Query guardrails

Limits on query cost and data scope, so an agent cannot turn an investigation into an incident of its own.

Implementation

Built fresh from public documentation for each engagement. Nothing carries between clients.

Scope

Where we are honest about fit.

Worth stating plainly, because it saves both of us a call.

Declined

  • Seat based staff augmentation. We deliver outcomes, not a resume in a seat.
  • An unsupervised autonomous coding system. Agents here operate under principal review.
  • Work without Java at the center. Java delivery is the practice.
  • Undefined hourly engagements without a scoped outcome. Engagements are fixed scope statements of work or monthly retainers against defined deliverables.
  • Permanent product ownership. Where a full time hire is the right answer, we say so.

Accepted

Corp to corp  ·  Fixed scope SOW  ·  Monthly retainer  ·  Through a staffing prime  ·  Direct with the end client

Next step

Start with the assessment

Two weeks, fixed scope, ending with a sequenced plan and a running MCP server over your codebase that you keep. Every engagement is led by the principal architect behind MNM Technology, who governs the Java and AWS delivery work end to end.

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