LockedIn Labs FDE
AI-native formation for engineers who intend to operate what they build—not merely talk about it.
How credibility is earned
From the field
Watching an expert work is not the same as doing the work. LockedIn Labs is built around that difference — a graph instead of a catalog, proof-of-work instead of completion, and a defined cohort operating model that activates only when a cohort is contracted and staffed.
Forward-deployed engineering is not a hybrid of sales engineer and consultant. What separates it is that the engineer carries one outcome all the way into production and then gives it away — and that ending is the part the job descriptions leave out.
Matt Pocock's public talks and open-source workflow make a useful point for AI-native engineers: faster generation raises the value of alignment, shared language, feedback, and deliberate system design.
AI Fieldcraft is the practice discipline inside LockedIn Labs: a repeatable path from unfamiliar capability to evidence, controls, release, and measured operation — without creating another course ladder or product.
A composite failure scenario shows how an ambiguous tool schema can turn a capable model into an unreliable operator — and why a common protocol is plumbing, not proof of contract quality.
Completion records participation. Proof-of-work can support a narrower, more useful claim: what a learner produced, the standard applied, and the provenance of the verdict.
Healthcare and financial services aren't anti-AI — they're anti-unaccountable. Treated as a design constraint rather than a blocker, regulation sharpens the system instead of slowing it.
A course can end at a certificate. LockedIn's standard aims at operating capability and requires evidence before it claims the work changed. Here is the design: the posture, the roles, the proof, and the continuing practice layer.
The work, mapped
Ten operating domains and 55 capability families define the field. The training beneath them shows where engineers build evidence.
Capability field
01 / 10FDE.DISCOVERY
Discover, frame, prioritize, and contract consequential enterprise problems under ambiguity.
6 capability families
Adapted from the released LockedIn FDE Benchmark Ontology v0.1.0, a public-draft, synthetic-only artifact licensed under CC BY 4.0. LockedIn Labs added the training map; the source ontology was not modified. This is not a benchmark result or official score.
The DEPLOY Method
DEPLOY turns a capable builder into an accountable field engineer. Every stage answers a different mission question and leaves evidence before authority expands.
How does the work actually move, especially where the documented process breaks?
Mission charter, stakeholder map, and observation boundary
What is the smallest safe wedge that can produce decision-grade evidence of workflow impact?
Outcome contract and wedge decision record
Can the smallest wedge meet the mission contract under representative, edge, adversarial, and changing conditions?
Runnable wedge and provider-neutral behavioral contract
Can the proven wedge live inside enterprise identity, data, control, and operating boundaries without losing its evidence?
Production-shaped enterprise integration slice
Can users adopt the system while the team detects, contains, and learns from behavior in production?
Production-readiness and staged-rollout record
Can the named owner operate, change, and recover the system—and what permitted learning should compound?
Owner-accepted transfer package and owner-executed change/recovery record
Evaluation
The work, cases, thresholds, and authority boundary are fixed before performance is evaluated. Later polish cannot erase an unsafe decision.
How the system evaluates
Freeze the mission specification, representative cases, graders, thresholds, and authority envelope before comparing implementations.
Run at least two materially different provider or runtime configurations through the same harness.
Compare task success, critical failures, retries, latency, review burden, and total cost per accepted outcome—not output style alone.
Document behavior that is truly portable, behavior that needs an adapter, and behavior that cannot be made equivalent without changing the mission contract.
Repeat the comparison after any provider, model, prompt, tool, policy, or retrieval change that can alter behavior.
Formation formats
12-week cohort formation is followed, where authorized, by up to 12-week supervised field residency. The 12–24 weeks window is an authored runway, not a demonstrated time-to-competence claim.
Build durable mental models and production artifacts through authored cases, executable work, critique, and revision.
Mission artifacts and decision records
Make hidden judgment visible through discovery, architecture, review, incident, and executive-room practice.
Observed performance record
Test adaptation when requirements, data, stakeholder priorities, system behavior, or risk change after work begins.
Complete evidence chain
Move from controlled performance to accountable delivery inside a real client boundary without placing unsupervised learners in consequential roles.
Client-owned mission deliverables
Compound judgment through artifact critique, case clinics, incident reviews, adapter comparisons, and field notes.
Peer critique and response record
Authority
Teaching, technical challenge, field supervision, and client acceptance remain distinct. No anonymous score collapses those responsibilities.
Owns the mission sequence, critique cadence, and remediation path.
Challenges implementation, evaluation, security, reliability, and recovery evidence.
Owns the safety boundary and records decisions observed in supervised work.
Accepts the mission and proves that ownership no longer depends on the engineer.
LockedIn Labs FDE
