LAB 01WORKFLOW
From Prompt to Controlled Workflow.
Decompose a complex Agent objective into observable, verifiable, recoverable units with explicit human authority.
FOCUSTask Contracts · Evidence Gates · Recovery
Open Lab 01 FDE career training · enterprise deployment labs
Learn the work between a prototype and operational value.
Practice forward deployed engineering through realistic company missions. Use your AI agent, investigate imperfect evidence, build a credible operating solution, and defend whether the system should deploy, change, or stop.
Leading a technical team? Build a shared deployment language →
04core deployment labs
08enterprise missions
FREEemail access during launch
CURRENT DECISION
“Should Freight Operations reroute the shipment before the decision window closes?”
01InvestigateEvidence reviewed
02FrameDecision defined
03BuildWorkflow active
04EvaluateTests pending
05DefendReview locked
Required artifactEvidence-backed deployment recommendation
READINESS64%
Learn the method. Build the evidence. Carry it into the team.
INVESTIGATEBRIEFVERIFYBUILDEVALUATEDEFEND
FREE CORE DEPLOYMENT LABS
Start with the decisions every production Agent eventually faces.
Nineteen interactive labs now cover the deployment path from workflow and evidence through release, operations, human authority, multi-Agent reconciliation, context boundaries, degraded capability, tool semantics, governed learning, durable memory, decision-state approval, delegated authority, replanning, evidence lineage, and independent human review. Each lab produces a concrete decision record rather than a completion badge.
LAB 02EVIDENCE
Evidence Under Pressure.
Reconcile credible sources that disagree and bound the strength of a decision to the evidence that actually supports it.
FOCUSReconciliation · Escalation · Claim Boundaries
Open Lab 02LAB 03RELEASE
Can This Agent Ship?
Go beyond Accuracy, test containment, set thresholds, and decide how much operational authority the evidence supports.
FOCUSEvaluation · Failure Containment · Release Scope
Open Lab 03LAB 04OPERATE
What Happens When the Agent Fails in Production?
Detect, triage, contain, reconstruct, recover, and resume after a real production failure changes the operating assumptions.
FOCUSIncident Response · Kill Switch · Safe Resume
Open Lab 04LAB 05DRIFT
The Agent Is Drifting.
Detect behavior drift, isolate the affected cohort, narrow authority, collect fresh evidence, and re-release only the scope the new evidence supports.
FOCUSBehavior Baseline · Segmentation · Re-release
Open Lab 05LAB 06HUMAN
Human Override.
Define when a human can overturn the Agent, what evidence the exception requires, who has authority, and how the decision remains auditable.
FOCUSEvidence · Authority · Exception Scope
Open Lab 06LAB 07OBJECTIVE
The Agent Did Exactly What You Asked.
Diagnose proxy optimization when the KPI improves but the business outcome degrades, then redesign success with counter-metrics and constraints.
FOCUSObjective Contract · Counter-Metrics · Change Governance
Open Lab 07LAB 08ARCHITECTURE
One Workflow, Three Models.
Allocate intelligence by task, route uncertainty upward, protect evidence diversity, and keep model capability separate from operational authority.
FOCUSModel Routing · Evidence Routing · Authority Map
Open Lab 08LAB 09DISAGREEMENT
The Agents Disagree.
Replace voting with claim decomposition, evidence reconciliation, policy provenance, and a stop rule for unresolved high-impact conflict.
FOCUSClaims · Reconciliation · Stop Rule
Open Lab 09LAB 10CONTEXT
The Right Answer for the Wrong Customer.
Keep correct but unauthorized evidence out of the decision context and test for hidden cross-customer influence.
FOCUSEvidence Authorization · Scope · Influence
Open Lab 10LAB 11DEGRADED
The Fallback Made It Worse.
When model capability degrades, reduce authority deliberately instead of treating fallback availability as equivalent assurance.
FOCUSCapability Profile · Authority Reduction · Recovery Gate
Open Lab 11LAB 12TOOL SEMANTICS
The Tool Did Not Mean What the Agent Thought.
Make API semantics, provenance, freshness, and decision authority explicit before tool output becomes Agent evidence.
FOCUSSemantic Contract · Tool Trust · UNKNOWN
Open Lab 12LAB 13LEARNING
The Agent Learned the Exception.
Let the Agent detect repeated overrides without letting repeated behavior silently become new policy or operational authority.
FOCUSLearning Boundary · Policy Review · Authority Drift
Open Lab 13LAB 14MEMORY
The Memory Was Wrong.
Govern durable Agent memory as evidence: trace provenance, preserve scope, mark superseded facts, and stop repeated derived memories from manufacturing their own corroboration.
FOCUSMemory Provenance · Supersession · Lineage
Open Lab 14LAB 15DECISION STATE
The Approval No Longer Applies.
Bind Human Approval to the evidence, action parameters, plan, and policy actually reviewed; invalidate authority when material state changes before execution.
FOCUSDecision State · Invalidation · Revalidation
Open Lab 15LAB 16DELEGATION
The Agent Delegated the Authority.
Trace authority through multi-Agent delegation, intersect child capability with the authority carried by the task, and block authority laundering across nested calls.
FOCUSAuthority Envelope · Lineage · Delegation Gate
Open Lab 16LAB 17REPLANNING
The Agent Found Another Way.
Distinguish retryable technical failure from authority denial, compare alternative paths by outcome, and stop locally allowed actions from composing into a forbidden effect.
FOCUSOutcome Policy · Plan Effect · Terminal Denial
Open Lab 17LAB 18EVIDENCE LINEAGE
The Agent Became Its Own Source of Truth.
Keep persisted Agent inferences marked as derived, trace descendants to independent evidence roots, and prevent stored model conclusions from self-corroborating.
FOCUSObserved vs Derived · Evidence Roots · Lineage
Open Lab 18LAB 19HUMAN REVIEW
The Human Approved the Summary.
Audit the evidence surface presented to a human, preserve decision-changing conditions and contradictions, and separate formal approval from independent review.
FOCUSDecision Sufficiency · Source Traceability · Review Gate
Open Lab 19Free during this stage. Enter your name and email once; we send a secure magic link that unlocks all nineteen Core Deployment Labs for 30 days.
Get free lab access01 / HOW THE LAB WORKS
We provide the company environment. You practice the field role.
Deploy to Value is not a prompt library or a passive video course. Each lab surrounds your AI agent with a controlled company scenario, traceable evidence, enterprise constraints, required artifacts, and a deployment review.
Enter the mission
Take responsibility for a specific operating decision inside a realistic company with stakeholders, systems, deadlines, and consequences.
INPUT · company contextWork with your agent
Use ChatGPT, Claude, Gemini, Cursor, a local model, or another capable agent to investigate, design, build, and test.
PROCESS · agent-assisted workVerify and defend
Trace conclusions to evidence, expose uncertainty, test the intervention, and recommend deploy, revise, limit, gather evidence, or stop.
OUTPUT · production decisionDEPLOY TO VALUE PROVIDES
Company brief, evidence room, constraints, staged complications, artifact standards, checks, rubric, and review pathway.
YOU PROVIDE
Your AI agent, investigation, implementation choices, verification, judgment, and accountability for the recommendation.
02 / START WITH THE FLAGSHIP
Freight Operations Case
A maritime delay system already exists, but dispatchers do not trust it. Reconstruct the decision, reconcile unstable source data, build the minimum credible workflow, and determine whether Freight Operations should scale it.
FORMATSelf-paced guided mission
TIMEBOX12–18 hours
LEVELFoundation
AGENTBring your preferred tool
03 / WHAT YOU PRODUCE
Build evidence you can use in interviews and field reviews.
Your record shows how you framed the problem, what evidence you trusted, where the agent was wrong, what you built, how you tested it, and why your final recommendation follows from the results.
01Use-case qualification memo
02Stakeholder and workflow map
03Evidence and assumption register
04Architecture decision record
05Working thin-slice implementation
06Evaluation and failure analysis
07Production-readiness plan
08Executive deployment recommendation
09AI collaboration and verification record
04 / DEPLOYMENT MISSIONS
Practice the same discipline across different operating systems.
Every case changes the industry, evidence, risks, and decision. The mission engine remains consistent so you can demonstrate transfer rather than memorize one solution.
MISSION 01LIVE LAB
AF
Freight Operations Case
Reconcile unstable maritime evidence and expose financial risk before the operating decision window closes.
DECISIONScale, revise, limit, or stop the delay workflow
Start the guided mission →
MISSION 02MANUFACTURING
NM
Manufacturing Quality Case
Unify sensor, material, maintenance, shift, and inspection evidence without overstating what correlations prove.
DECISIONWhich factor should the plant investigate first?
Read the case specification →
MISSION 03PRODUCTION AI
TI
Industrial Knowledge Case
Deploy a governed technical knowledge assistant that remains useful without turning unsafe field notes into authority.
DECISIONWhen should the assistant answer, abstain, or escalate?
Read the case specification →
Four additional public case briefs
05 / DEPLOYMENT METHOD
One operating discipline across every mission.
The technology and industry change. The accountability does not. Every case moves through the same six-stage deployment lifecycle.
Discover
Observe the workflow, identify the decision, map stakeholders, and establish a baseline.
OUTPUT · problem frameDesign
Choose the smallest intervention that can change the decision under real constraints.
OUTPUT · solution thesisBuild
Connect data, logic, interfaces, permissions, and feedback into one credible workflow.
OUTPUT · deployable sliceDeploy
Handle security, integration, observability, failure modes, ownership, and rollout.
OUTPUT · production planAdopt
Design human authority, training, overrides, support, and exception handling into the system.
OUTPUT · operating changeMeasure
Compare results to the baseline and decide whether to scale, revise, constrain, or stop.
OUTPUT · value proof06 / ONE METHOD, THREE LEVELS
Build practitioners. Align teams. Govern AI operations.
Deploy to Value grows from individual capability to organizational capability and, ultimately, human-governed AI operations. Every level uses the same deployment lifecycle, evidence standards, and decision language.
Become field-ready.
Learn the Core Method free, then apply it inside realistic Deployment Missions with evidence rooms, staged complications, artifacts, and final release decisions. Mission access remains free during launch; standard individual pricing begins at $49, with the full practitioner library planned at $199/year.
Explore individual programs →Give the team one language.
Align engineering, AI, product, security, implementation, and leadership around shared artifacts, gates, reviews, and deployment decisions.
Explore team programs →Make deployment governance executable.
Use deployment contracts, evidence capture, scoring, authority gates, monitoring, and rollback to control production AI workflows.
Explore Deploy to Value Runtime →CURRENT ENTRY POINT
Start by learning how an FDE makes the deployment defensible.
Enter Freight Operations Case, examine the evidence, and produce your first enterprise deployment decision.