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# PILOT_OFFER_ONE_PAGER
Pilot collaboration entry for teams exploring WFGY in real AI workflows.
This page is a compact, buyer-facing summary of what a WFGY pilot can look like.
It is written for teams who already have a real system, a real failure pattern, or a real evaluation problem, and want to test whether WFGY is useful in practice.
For the broader collaboration entry, see [WORK_WITH_WFGY.md](./WORK_WITH_WFGY.md).
For a historical view of how WFGY became publicly legible, see [EVIDENCE_TIMELINE.md](./EVIDENCE_TIMELINE.md).
For a sample output shape, see [SAMPLE_DELIVERABLE.md](./SAMPLE_DELIVERABLE.md).
---
## What this page is
This page is a practical pilot overview.
Its job is simple:
help a serious team answer three questions quickly:
1. Is WFGY relevant to our situation
2. What would a small pilot actually look like
3. What would we likely get back at the end
This is not a pitch deck, not a customer logo page, and not a promise of enterprise deployment.
---
## Who this is for
WFGY pilots are best suited for teams that already have one of the following:
* a RAG system that keeps returning wrong answers even when infra looks normal
* an agent or multi-agent workflow with unstable behavior, drift, or brittle handoffs
* an evaluation workflow that can score outputs, but still cannot clearly explain failure structure
* a debugging process that is expensive, slow, and overly dependent on ad hoc intuition
* a research or platform team that wants a more structured way to classify failure modes
In short, this page is for teams with real questions, not for people looking for generic prompt advice.
---
## What WFGY is most useful for in a pilot
At the current stage, the strongest practical wedge for WFGY is structured diagnosis.
That usually means one or more of the following:
* classifying recurring failure modes in a RAG or agent pipeline
* separating retrieval, prompt assembly, orchestration, memory, and evaluation failures
* building a more stable debugging vocabulary across engineers, PMs, and researchers
* turning scattered symptoms into a smaller set of reproducible failure categories
* reducing guesswork before a team spends time on bigger architectural changes
This is especially useful when a team already knows that “something is wrong,” but cannot yet describe the failure in a way that leads to clean fixes.
---
## Pilot formats
A WFGY pilot will usually fit one of these formats.
### 1. Failure audit pilot
Best for teams with a live or recently failing RAG or agent workflow.
Typical goal:
map observed failures into a smaller set of structured categories, identify the likely layer where the problem actually lives, and suggest the smallest next debugging moves.
Typical inputs:
* failing examples
* run traces, logs, screenshots, or prompt chains
* brief architecture description
* known symptoms and current hypotheses
Typical outputs:
* structured failure classification
* likely root-cause layer analysis
* fix priority suggestions
* a clearer debugging route for the team
---
### 2. Triage workshop pilot
Best for teams that need fast alignment across internal stakeholders.
Typical goal:
use WFGY surfaces such as the Problem Map or Global Debug Card to create a shared language for triage, review, and prioritization.
Typical inputs:
* representative failure cases
* current internal workflow for debugging or review
* participating team roles
* constraints on time, tooling, or ownership
Typical outputs:
* a shared failure vocabulary
* a smaller triage decision surface
* candidate routing rules for common cases
* a cleaner handoff structure across team members
---
### 3. Design partner pilot
Best for teams exploring deeper protocol, tooling, or evaluation integration.
Typical goal:
test whether WFGY can serve as part of a reusable debugging, evaluation, or reasoning layer inside a broader product or research workflow.
Typical inputs:
* a clear use case
* target surface for integration or evaluation
* baseline workflow or benchmark
* practical constraints and success criteria
Typical outputs:
* pilot framing document
* integration hypotheses
* structured observations from the trial
* recommendation on whether deeper work is justified
---
## What a team usually needs to provide
A good pilot depends on concrete material.
The team does not need to provide everything at once, but a serious pilot usually needs:
* one clear system or use case
* several representative failures or stress cases
* enough context to understand where the system boundaries are
* the current debugging or evaluation workflow, even if it is messy
* one contact point who can answer follow-up questions
If the pilot is about a production system, confidentiality and scope should be discussed early.
---
## What WFGY usually provides
A WFGY pilot usually provides structure, not magic.
That structure may include:
* a clearer failure map
* a smaller set of meaningful categories
* sharper distinctions between surface symptoms and deeper causes
* a more reproducible debugging route
* a shared interpretive layer that makes future failures easier to discuss
Where relevant, WFGY may also provide draft artifacts such as:
* a case classification sheet
* a triage summary
* a debug routing proposal
* an evaluation framing note
* a recommended next-step sequence
For an example of the shape of outputs, see [SAMPLE_DELIVERABLE.md](./SAMPLE_DELIVERABLE.md).
---
## What this does not claim
A WFGY pilot does not automatically mean:
* full production integration
* guaranteed model quality improvement
* enterprise-grade support or SLA
* replacement of platform engineering, ML engineering, or security review
* one-step diagnosis of every failure in a complex system
WFGY is most useful when it helps a team see the failure structure more clearly.
That often improves decision quality, but it should not be described as a universal fix.
---
## Good fit and bad fit
### Good fit
A pilot is usually a good fit when:
* the team has real failure cases
* the problem is costly enough to matter
* the team wants sharper structure, not vague brainstorming
* the team is open to disciplined boundary-setting
* the team can provide enough evidence to reason from
### Bad fit
A pilot is usually a poor fit when:
* there is no concrete system yet
* the team only wants generic prompting advice
* the team wants guaranteed outcomes before sharing any evidence
* the problem is actually legal, security, compliance, or infra ownership only
* the team expects WFGY to replace core implementation work
---
## Suggested pilot flow
A small pilot can often be framed in four stages:
1. Scope
define the system, the problem surface, and the pilot question
2. Evidence intake
review examples, traces, and known symptoms
3. Structured analysis
map failures, isolate likely layers, and identify the most useful distinctions
4. Return package
provide a compact summary of findings, boundaries, and recommended next moves
This is intentionally small.
The purpose of a pilot is not to pretend the whole system is solved.
The purpose is to learn whether WFGY creates real clarity and practical leverage.
---
## Best current reading of WFGY pilot value
Today, the safest and strongest claim is this:
WFGY is most legible as a structured reasoning and debugging layer for AI systems, especially where teams need better failure classification, cleaner triage, and more reproducible diagnosis.
That is the right starting point for a pilot.
Broader claims should only be made if later evidence supports them.
---
## Next step
If your team is exploring a pilot, start here:
* [WORK_WITH_WFGY.md](./WORK_WITH_WFGY.md) for the broader collaboration entry
* [CASE_EVIDENCE.md](./CASE_EVIDENCE.md) for how public cases should be read
* [ADOPTERS.md](./ADOPTERS.md) for the shortest public proof summary
If needed, this page can later evolve into a more formal outward-facing pilot brief.
For now, its role is simpler:
to make the pilot path legible without overselling it.

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# SAMPLE_DELIVERABLE
Sample structure of a compact WFGY pilot return package.
This page shows what a small WFGY deliverable may look like after a pilot, audit, or structured review.
It is not a promise that every engagement will produce the same sections in the same length.
It is a practical sample that makes the expected output shape easier to understand before collaboration begins.
For the pilot entry itself, see [PILOT_OFFER_ONE_PAGER.md](./PILOT_OFFER_ONE_PAGER.md).
For the broader collaboration entry, see [WORK_WITH_WFGY.md](./WORK_WITH_WFGY.md).
For historical context and public proof, see [EVIDENCE_TIMELINE.md](./EVIDENCE_TIMELINE.md).
---
## What this page is
This page is a sample return package.
Its purpose is to answer a simple but important question:
**what would a team actually receive after a small WFGY pilot**
The answer is not “a vague summary.”
The intended shape is a bounded, structured, decision-useful package that helps a team move from scattered symptoms toward clearer categories, clearer boundaries, and more disciplined next steps.
This page is not:
* a fixed legal scope
* a formal statement of work
* a guarantee of outcome
* a claim that every engagement will uncover the same level of clarity
It is a model of what “useful structure” can look like.
---
## Best way to read this sample
Read this page as a sample in three senses at once:
### 1. Structure sample
It shows the sections a compact WFGY return package is likely to include.
### 2. Decision sample
It shows the kind of judgments WFGY tries to make legible:
* what the likely failure layers are
* what is high confidence versus low confidence
* what next moves are worth doing first
### 3. Boundary sample
It shows that a good deliverable should not only say what seems likely.
It should also say what remains uncertain and what is out of scope.
---
# Sample WFGY Return Package
## 1. Engagement snapshot
**Project type**
RAG or agent workflow review
**Pilot type**
Failure audit pilot
**Review scope**
Small scoped review based on a limited set of representative failures, system notes, and current debugging assumptions
**Primary question**
Why does the system continue to produce wrong, unstable, or weakly grounded outputs even when the infrastructure appears mostly healthy
**Deliverable goal**
Convert scattered symptoms into a smaller set of structured categories, identify the most likely failure layers, and recommend a practical next-step sequence
**Overall reading**
The system does not appear to be facing one isolated issue.
The evidence suggests a layered debugging problem with multiple interacting surfaces.
---
## 2. Inputs reviewed
The following materials were reviewed in this sample scenario:
* representative failing examples
* selected logs, traces, screenshots, or prompt chains
* a short description of the current architecture
* the team’s current explanations or debugging hypotheses
* key constraints on ownership, tooling, and deployment
### Boundary note
The pilot does not assume full access to every production component.
The goal is to review enough material to reach a disciplined structural reading, not to claim omniscience over the whole system.
---
## 3. System snapshot
The reviewed system is a retrieval-backed generation workflow with a multi-step prompt construction path, a document retrieval layer, a ranking layer, and a final answer-generation stage.
The team reports the following recurring pattern:
* some answers are fluent but incorrect
* similar questions may produce different retrieved evidence and different final outputs
* some failures appear before final generation
* debugging discussions often collapse multiple error types into one generic label
### Why this section exists
This section is intentionally short.
Its purpose is to establish a readable shared context before moving into classification and judgment.
---
## 4. Observed failure surface
Before any deeper interpretation, the visible failure surface in this sample case looks like this:
1. The system often produces answers that sound stable but are not reliably grounded in the retrieved material.
2. Similar inputs do not consistently lead to similar retrieval and answer behavior.
3. Evidence suggests that some failures emerge before final answer generation, especially in selection or context preparation.
4. The current debugging loop appears to focus heavily on the model output itself, while upstream layers may be contributing materially to the final result.
### Observational status
This section stays close to visible behavior.
It does not yet claim root cause.
---
## 5. Structured failure classification
This is one of the core sections in a WFGY-style return package.
### Primary category cluster
#### A. Retrieval-selection instability
Relevant material is not being surfaced consistently enough across similar requests.
**Confidence**
High
**Why this appears likely**
Repeated variation in retrieved context suggests the issue begins upstream of final generation in at least part of the case set.
#### B. Context assembly distortion
Useful material may exist, but the way it is combined, compressed, ordered, or weighted may reduce its practical usefulness before generation.
**Confidence**
Medium to high
**Why this appears likely**
Some failures show a gap between the presence of relevant source material and the quality of the final answer.
#### C. Final-answer overconfidence
The answer layer sometimes presents weakly supported outputs with stronger confidence than the evidence can justify.
**Confidence**
Medium
**Why this appears likely**
Observed outputs appear rhetorically stable even when grounding is partial or inconsistent.
### Secondary category cluster
#### D. Evaluation blind spots
The current review loop may detect bad outcomes, but does not yet separate retrieval, orchestration, and answer-layer failures reliably enough.
**Confidence**
High
#### E. Triage vocabulary weakness
Multiple distinct failure patterns may be grouped under one generic description, making debugging slower and less reproducible.
**Confidence**
High
### Why this section matters
This section converts raw symptoms into smaller, reusable buckets.
That matters because teams often lose time not only from technical issues, but from category confusion.
---
## 6. Likely root-cause layers
This section moves from classification toward deeper reading.
### Highest-probability layers
#### 1. Retrieval and selection layer
**Priority**
Highest
**Confidence**
High
**Reading**
At least part of the observed failure surface likely begins before the model writes the final answer.
#### 2. Context construction layer
**Priority**
High
**Confidence**
Medium to high
**Reading**
The prompt may be receiving technically relevant material in a structurally degraded form.
#### 3. Review and evaluation layer
**Priority**
High
**Confidence**
High
**Reading**
The current internal debugging loop may not yet distinguish failure signatures by layer clearly enough to support fast iteration.
### Lower-confidence but relevant layers
#### 4. Memory or carryover behavior
**Priority**
Medium
**Confidence**
Low to medium
#### 5. Tool or handoff instability
**Priority**
Medium
**Confidence**
Low to medium
#### 6. Prompt framing side effects
**Priority**
Medium
**Confidence**
Low
### Interpretation rule
A strong deliverable should separate:
* what appears likely
* what remains possible
* what is still too weak to assert
That distinction is part of the value.
---
## 7. Working diagnosis
### Core reading
The current pattern does not look like a pure model-quality problem.
The stronger reading is that this is a layered systems problem in which retrieval quality, context assembly, and evaluation framing interact to produce unstable or weakly grounded final answers.
### Why this matters
If the team continues to read the issue only as “the model hallucinated,” it may keep applying fixes at the wrong layer.
The evidence in this sample case suggests that the more useful route is to separate the failure surface into upstream selection, context construction, and final-answer expression.
### Boundary
This is a working diagnosis, not a claim of full proof.
---
## 8. Recommended next moves
This section should be concrete, limited, and sequenced.
### Priority 1
Separate retrieval failure from generation failure using a smaller reviewed case set.
**Goal**
Stop treating all bad answers as one category.
**Why first**
This creates the cleanest structural gain for the least cost.
### Priority 2
Inspect context assembly rules for compression, ranking, truncation, and ordering artifacts.
**Goal**
Check whether useful material is being technically retrieved but practically neutralized before generation.
**Why second**
This is one of the most likely places where “good inputs turn into weak answer conditions.”
### Priority 3
Add a lightweight layer tag to internal review.
**Goal**
Mark each failure as most likely retrieval, assembly, answer, tool, memory, or evaluation related before discussing fixes.
**Why third**
A small tagging habit often improves debugging clarity more than another round of vague brainstorming.
### Priority 4
Standardize a short internal vocabulary for repeated failure classes.
**Goal**
Reduce repeated ambiguity in triage conversations.
**Why fourth**
This makes future failures cheaper to discuss and faster to route.
---
## 9. What remains uncertain
A good deliverable should say clearly what it does not yet know.
In this sample scenario, the reviewed material is sufficient for a structured preliminary reading, but not sufficient for strong claims about all long-run production behavior.
### Still uncertain
1. Whether ranking logic or chunking logic is the dominant upstream driver
2. Whether carryover or memory effects are meaningful or only incidental
3. Whether some observed failures are benchmark-specific rather than architecture-level
4. Whether the same pattern holds consistently across all major workload classes
### Why this section matters
Without an uncertainty section, teams often over-read a pilot and treat it as a full-system verdict.
That would be a mistake.
---
## 10. Boundaries and non-claims
A compact WFGY return package should clearly state what it does **not** establish.
This sample does not claim:
* that every major failure has been found
* that all root causes are proven
* that the system is near production readiness
* that architecture changes are unnecessary
* that a small pilot replaces engineering, security, or infrastructure work
* that every future failure will fit the same categories
The purpose of the package is narrower and more practical:
to improve structural clarity, reduce debugging ambiguity, and make the next round of decisions more disciplined.
---
## 11. Possible follow-on outputs
Depending on scope, a future engagement may extend into outputs such as:
* a cleaner internal failure taxonomy
* a triage worksheet for recurring incidents
* a review rubric for future runs
* a routing guide for common failure types
* a summary note for decision-makers
* a deeper design-partner or integration proposal
These are possible extensions.
They are not automatic promises.
For the pilot framing that may lead into these, see [PILOT_OFFER_ONE_PAGER.md](./PILOT_OFFER_ONE_PAGER.md).
---
## 12. Why this sample matters
Many teams do not need more generic advice.
They need a better way to move from messy evidence to smaller, more meaningful decisions.
That is the role of a WFGY deliverable at its best.
It helps a team move from:
**something is wrong**
toward:
**these are the likely failure layers, these are the boundaries, and these are the next moves worth trying**
That is a much better place to be.
---
## Related pages
* [PILOT_OFFER_ONE_PAGER.md](./PILOT_OFFER_ONE_PAGER.md)
* [WORK_WITH_WFGY.md](./WORK_WITH_WFGY.md)
* [CASE_EVIDENCE.md](./CASE_EVIDENCE.md)
* [ADOPTERS.md](./ADOPTERS.md)
* [EVIDENCE_TIMELINE.md](./EVIDENCE_TIMELINE.md)
---
Maintained as a sample structure, not a fixed contract.