Least context, by role
Each agent sees the layers and documents its job is allowed to use, and nothing more.
Agentic software engineering
Turn intent into verified software.
Mojo Factory coordinates specialist AI agents inside a deterministic engineering harness, working from your governed context. Approved behaviour sets the boundary, an architecture agent protects the design, and four evidence pillars decide whether a change moves forward.
Mission CR-0241Settlement exception workflow
Review in progress
- ArchitectBoundaries and ADR approvedsealed
- LeadPlan split into 3 bounded tasksdone
- ExecutionChange within approved pathsdone
- Verification14 scenarios on the real path14 / 14
- QARegression and performance checksrunning
- DevOpsRelease under policywaiting
Functional ✓Design ✓Implementation ✓Assurance…
The factory difference
AI that writes code is easy. AI that proves its change is safe is the hard part.
Unbounded generation
A model edits whatever it can reach
Changes spread past the request, tests confirm their own fixtures, and success is whatever the agent reports. Review becomes the only safeguard.
Mojo Factory
Every change runs inside three limits
- WhatApproved behaviour defines what may change.
- WhereThe architecture defines where it may change.
- WhetherFactory-made evidence decides whether it proceeds.
Governed context
Agents work from your context, and it is governed like code
Most AI tools either guess at your business or need everything pasted in. Mojo Factory keeps your knowledge in layers, gives each agent only what its job needs, and records exactly which version every decision relied on.
CompanyStandards, policies, architecture principles
ClientDomain rules, regulations, integration terms
ProjectDesign, decisions, repositories, conventions
TicketThe approved intent, scenarios and constraints
Owned and approved
Every layer has owners. Agents may propose a change to context; a person with approval rights accepts it.
Versioned and verified
Context is versioned and fingerprinted, so a stale or altered document is caught before an agent uses it.
Traceable decisions
Every artifact and verdict records the context versions it relied on, so you can see why a change was made.
Stays in your boundary
Context lives inside your factory and is sent only to the models you approve for each job.
No context rot
Long-running work picks up the current approved context at every stage, instead of drifting on an old conversation.
Core capabilities
Six things the factory does on every change
BDD-first delivery
Behaviour is written and approved as scenarios before any code. The scenarios become the contract the change is judged against.
Architecture as an agent
A dedicated agent holds the system design and decisions, and stops changes that cross a boundary it has not approved.
Multi-agent engineering
Specialist agents plan, build, test and release. No agent grades its own work, and each one works only inside its role.
Four-pillar verification
Functional, design, implementation and system assurance are checked separately, each with evidence the factory produces itself.
Sovereign execution
The factory runs inside your trust boundary, with your keys and your data. Nothing is pooled across clients.
Replaceable model fabric
Work is routed to approved models by capability, latency and cost. Swap a model without rewriting a single workflow.
Specialist agents
A team of agents, each with one job and clear limits
Sets the boundaries
Defines components, interfaces and the decision records every change must respect.
Plans and coordinates
Turns approved intent into bounded tasks and keeps the mission moving through each stage.
Implements the change
Writes code only within the approved paths and scope for the task.
Runs the evidence plan
Executes scenarios against the real system and records proof of what actually ran.
Tests system behaviour
Covers regression, edge cases and the behaviour around the change, not only the change itself.
Releases under policy
Commits, opens pull requests and releases only when the gates allow it.
Lifecycle
From approved intent to a decision you can audit
Define behaviour
Scenarios capture what must be true, and the product owner approves them.
Protect architecture
The architect agent confirms where the change may go before any code is written.
Coordinate implementation
Agents build in parallel, each inside its own role and paths.
Verify and decide
Evidence from all four pillars decides whether the change is accepted.
When a check fails, the work goes back to the responsible agent with a bounded number of retries and a circuit breaker. Every attempt is kept in the audit trail.
Four pillars of evidence
A change is accepted only when all four agree
Does it do the right thing?
Functional
Approved BDD scenarios pass against the running system.
Does it fit the design?
Design
Architecture conformance against the approved boundaries and decisions.
Is the code sound?
Implementation
Code correctness, review findings resolved, and tests that can actually fail.
Is the system still safe?
System assurance
Regression, security, performance and resilience checks.
Sovereignty
Your factory runs inside your boundary
Each client gets an isolated factory. Code, data and evidence stay where your policies say they belong.
- Deploys in your VPC, private cloud or on-premises.
- No data pooled across clients, ever.
- Client-owned encryption keys.
- Policy enforced by the factory, outside the model, so a model cannot talk its way past it.
Model fabric
The right model for each task, and freedom to change it
Tasks are routed by capability to the models you approve, balancing quality, latency and cost. Replace a model and every workflow keeps working.
Factory CoreThe harness, gates and agents
Technology PackReusable stack knowledge
Client PackYour rules, context and models
Client FactoryIsolated, with no product fork
Human intervention
People step in as much or as little as the risk requires
Observer
Watch the mission and its evidence without stepping in.
Advisor
Comment and guide while the agents keep the work.
Actor
Take on a step yourself, under the same gates.
Adaptive
Evidence and risk decide when a person is brought in.
Questions
Mojo Factory, in plain answers
What is Mojo Factory?
Mojo Factory is an agentic software engineering platform from TechMojo Solutions Private Limited. It coordinates specialist AI agents inside a governed engineering harness, so approved behaviour, protected architecture and evidence the factory produces itself decide whether a code change ships.
How is Mojo Factory different from an AI coding assistant?
A coding assistant generates code and reports success. Mojo Factory limits every change to approved BDD scenarios and architecture boundaries, gives each agent one role, never lets an agent grade its own work, and accepts a change only when functional, design, implementation and system-assurance evidence all agree.
How does Mojo Factory handle company context?
Context is kept in Company, Client, Project and Ticket layers. Each agent receives only the context its job needs, every layer has owners who approve changes, and context is versioned and fingerprinted so each decision records exactly which version it relied on.
Where does Mojo Factory run, and where does my code go?
Each client gets an isolated factory that runs inside their own boundary: their VPC, private cloud or on-premises. Data is never pooled across clients, encryption keys are client-owned, and context is sent only to the models the client approves for each job.
Which AI models does Mojo Factory use?
Work is routed by capability to the models each client approves, balancing quality, latency and cost. Models can be replaced without rewriting workflows.
Can people step in during a change?
Yes. People can observe, advise, take on a step themselves under the same gates, or let evidence and risk decide when a person is brought in.
Who builds Mojo Factory?
Mojo Factory is owned and built by TechMojo Solutions Private Limited, with offices in Hyderabad, Dubai and San Francisco.
Mojo Factory · built by TechMojo
See how a governed software factory would work for your team.
We will walk through a real change, from approved scenario to accepted release, with every piece of evidence on screen.