AI-FDE is a stateful operating system for internal AI teams and Forward Deployed Engineering leaders. It turns fragmented enterprise evidence into a verified operating model, helps a human FDE redesign a workflow, quantifies the business case, and produces a version-pinned implementation packet for engineering.
The project explores a core problem in AI-native delivery:
Before autonomous agents can safely build or automate a business workflow, the system needs trustworthy implementation intent.
AI-FDE is the governed layer between messy enterprise reality and downstream execution.
Evidence
→ candidate claims
→ human review
→ verified operating model
→ approved current workflow
→ Human / Software / AI allocation
→ approved target workflow
→ reproducible economics
→ implementation-ready artifacts
Enterprise AI projects rarely fail because a model cannot generate code. They fail because implementation begins from incomplete context, undocumented exceptions, weak economics, unclear authority, or requirements that were never verified against how the business actually operates.
AI-FDE is designed to make that discovery-to-delivery path repeatable, auditable, evidence-backed, and measurable.
- ingests enterprise evidence into an engagement-scoped workspace
- extracts bounded candidate claims instead of silently promoting generated text to truth
- preserves exact source provenance for human review
- surfaces contradictions, unknowns, rules, approvals, and exception paths
- builds a typed and versioned Company Operating Model / Business Twin
- separates current-state workflow from target-state design
- makes Human / deterministic software / AI allocation explicit
- models low/base/high economics from stored assumptions and formulas
- generates version-pinned implementation artifacts and acceptance criteria
- tracks model tokens, latency, cost, and accepted outcomes
- preserves audit history and human approval boundaries
A coding agent can implement a precise specification that is precisely wrong.
AI-FDE therefore keeps four things separate:
- Evidence — what source material actually says
- Inference — what AI proposes from that evidence
- Approval — what a human accepts as authoritative
- Execution intent — what downstream engineering systems are allowed to build
That separation creates a safer bridge from enterprise discovery to AI-native engineering execution.
- Evidence may create candidate claims but cannot directly create verified truth.
- Material assertions must resolve to stored evidence.
- Model confidence and human verification are separate.
- Contradictions remain visible until explicitly resolved.
- Approved versions are immutable.
- Upstream changes stale dependent downstream artifacts.
- Economic outputs reproduce from stored inputs and formulas.
- Customer records remain engagement-scoped.
- Long-running work is persistent, retryable, and observable.
- Uploaded content is untrusted input and cannot expand system authority.
AI-FDE and Mission Control solve different layers of the same broader problem.
AI-FDE turns uncertain enterprise evidence into governed implementation intent.
Mission Control turns governed implementation intent into bounded agent execution, verification, evidence, pull requests, and human decisions.
Enterprise evidence
↓
AI-FDE
↓
Verified implementation intent
↓
Mission Control / Software Factory
↓
Bounded agent execution
↓
Verification + evidence
↓
Human-approved delivery
Internal-alpha code candidate; synthetic-data only.
The synthetic evidence-to-specification path is implemented across multiple workflow shapes with structured delivery assessments and an objective program scorecard. External production gates—including live identity/cloud controls, restore/deletion evidence, secret rotation, and model-evaluation evidence—remain intentionally fail-closed.
The repository does not claim production readiness or realized customer ROI. Current economics are scenario-based and reproducible from versioned inputs.
- stateful agent workflows
- evidence provenance and human verification
- typed operating models / knowledge graphs
- contradiction and uncertainty management
- deterministic economics
- stage-gated workflow state machines
- durable execution and recovery
- tenant/engagement isolation
- evaluations and outcome-linked model telemetry
- versioned implementation artifacts
- least-privilege authority boundaries
Make Forward Deployed AI Engineering faster without making it less trustworthy: preserve the evidence, decisions, economics, controls, and acceptance criteria required to move from discovery to implementation with confidence.