The FUCTEG Framework: A Proprietary A2O Model for Agent-Ready B2B Offers. Agent-Ready Offer Optimization for AEO, A2A and Direct RFQ

As B2B buying moves toward answer engines, AI agents and machine-to-machine transactions, companies need a new way to evaluate whether their products and offers are ready for agentic commerce.

Traditional SEO measures whether a page can be discovered by a search engine.

Traditional product information management measures whether product data is complete.

Traditional e-commerce measures whether a human user can understand an offer and complete a purchase.

These approaches remain important, but they are no longer sufficient.

An AI agent must be able to:

find the supplier and product,
understand the offer,
compare it with alternatives,
verify claims and evidence,
perform permitted commercial actions,
operate within defined business controls.

This is the purpose of A2O — Agent-Ready Offer Optimization.

A2O is the process of preparing a company, product, offer and transaction layer so that AI agents can reliably discover, interpret, evaluate, verify and use them.

Our proprietary A2O methodology is based on six criteria:

FUCTEG

F — Findable
U — Understandable
C — Comparable
T — Trustworthy
E — Executable
G — Governed

Together, these six dimensions describe the full journey from agent discovery to controlled transaction execution.

1. Findable

Can the agent find the company, product and required resources?

Findability is the first condition of agent readiness.

A product may be technically excellent and commercially competitive, but it will not participate in an AI-mediated buying process if an agent cannot discover it.

Findability includes more than ranking in Google.

An agent should be able to locate:

the company,
the supplier profile,
the product category,
the specific product,
the relevant use case,
technical and commercial documents,
a sales or technical contact point,
an RFQ form,
an API,
an MCP tool,
an A2A endpoint,
an order or service status interface.

Key assessment questions

Can the company be found by its legal name, brand name and product categories?
Can the product be discovered by name, SKU, MPN, GTIN, technical parameter or application?
Can an agent find the correct product page rather than a generic category page?
Are important documents indexed and linked to the correct product?
Is there a clear path to request a quotation?
Are machine-accessible endpoints documented?
Can an agent discover the supplier’s capabilities?

Findability components

Entity visibility

The company should be represented consistently across:

its website,
business directories,
industry portals,
partner websites,
product databases,
marketplaces,
professional profiles,
structured data.

Product discovery

Products should be discoverable through:

names,
identifiers,
categories,
attributes,
applications,
compatibility relationships,
common synonyms,
industry terminology.

Technical discovery

The agent should be able to locate:

product feeds,
APIs,
MCP servers,
A2A Agent Cards,
RFQ endpoints,
documentation resources.

Typical Findability failures

products are visible only inside PDF catalogues,
different websites use inconsistent product names,
important products have no individual pages,
documents are stored without product references,
internal SKU is the only available identifier,
the RFQ process is hidden behind a generic contact form,
no endpoint or capability description exists.

Desired outcome

The agent can discover the right supplier, product, document and action path without relying on guesswork.

2. Understandable

Can the agent correctly interpret what the product is?

Discovery does not guarantee understanding.

A product may be visible but still impossible to qualify because its description is vague, inconsistent or incomplete.

Understandability means that the product has a clear semantic and technical representation.

An agent should understand:

what the product is,
what category it belongs to,
what parameters define it,
what it is used for,
where it should not be used,
which variants exist,
which units apply,
what conditions affect its performance.

Key assessment questions

Does the product have one unambiguous name?
Is the manufacturer, brand, model and version clearly identified?
Are technical parameters stored as structured values?
Are units separated from values?
Are tolerances and operating conditions defined?
Are applications and prohibited uses explained?
Are variants represented as separate purchasable items?
Can an agent distinguish the product from accessories, alternatives and successors?

Understandability components

Product identity

The product should have:

a stable product ID,
product name,
manufacturer,
brand,
model,
SKU,
MPN,
GTIN where applicable,
version,
lifecycle status.

Classification

The product should be mapped to:

an internal category,
ETIM where relevant,
eCl@ss where relevant,
UNSPSC where relevant,
industry-specific codes,
application categories.

Structured parameters

Each important parameter should contain:

parameter ID,
name,
value,
unit,
tolerance,
minimum,
maximum,
measurement conditions,
source.

Application context

The product record should explain:

recommended use,
industry,
process,
operating conditions,
limitations,
prohibited applications.

Typical Understandability failures

product descriptions use only marketing language,
dimensions are written as free text,
units are missing,
several variants share one page and one identifier,
there is no distinction between nominal and guaranteed values,
compatibility is implied rather than defined,
product status is unclear,
the same term means different things in different documents.

Desired outcome

The agent can create an accurate internal representation of the product and determine whether it may fit the buyer’s requirement.

3. Comparable

Can the agent compare the offer with realistic alternatives?

A product becomes commercially useful to an agent only when it can be evaluated against other options.

Comparability is not limited to price.

A B2B purchasing decision may depend on:

technical parameters,
performance,
minimum order quantity,
total cost of ownership,
lead time,
delivery conditions,
compatibility,
warranty,
service,
compliance,
operational risk.

Key assessment questions

Can the agent compare equivalent technical attributes?
Are parameters expressed in standard units?
Is the price public, quantity-based, contractual or available only through RFQ?
Is MOQ clearly stated?
Can the agent compare delivery time and logistics cost?
Is compatibility defined?
Are warranty and service conditions structured?
Can the agent estimate TCO rather than only purchase price?
Are deviations from the requested specification clearly identified?

Comparable components

Technical comparison

The product should support comparison of:

dimensions,
material,
capacity,
performance,
durability,
operating range,
efficiency,
tolerances,
standards.

Commercial comparison

The offer should expose or explain:

price mode,
currency,
price validity,
quantity tiers,
MOQ,
order multiples,
packaging units,
freight,
payment terms,
Incoterms.

Operational comparison

The agent should be able to compare:

lead time,
availability,
installation requirements,
service response time,
spare-parts availability,
maintenance requirements,
product lifetime.

TCO comparison

For machines, systems and industrial products, TCO may include:

purchase cost,
consumables,
energy,
labour,
maintenance,
downtime,
training,
parts,
disposal.

Typical Comparability failures

suppliers use different units for the same parameter,
the public price excludes mandatory accessories,
MOQ is not disclosed,
lead time is described only as “fast delivery,”
warranty conditions are hidden in a general document,
performance claims have no common test method,
substitute products are labelled as equivalent without conditions,
the quote does not separate product, transport and service costs.

Desired outcome

The agent can compare offers on a like-for-like basis and explain why one option is better for a specific purchasing context.

4. Trustworthy

Can the agent verify that the product and offer are credible?

AI agents should not rely only on supplier claims.

A trustworthy product is supported by verifiable evidence.

The agent should be able to confirm:

who manufactured the product,
which documents apply,
whether those documents are current,
which version of the product they cover,
whether test results support the declared parameters,
whether references and case studies are credible,
whether independent confirmations exist.

Key assessment questions

Are TDS, SDS, declarations and certificates available?
Are documents assigned to the correct product and version?
Do documents include issue dates and revision numbers?
Is the document issuer identified?
Are test methods and conditions specified?
Are case studies connected to real use cases?
Are manufacturer data and distributor data consistent?
Are claims independently verified where required?
Can outdated documents be distinguished from current ones?

Trustworthiness components

Product evidence

Evidence may include:

TDS,
SDS,
operating instructions,
declarations of conformity,
declarations of performance,
regulatory statements,
certificates,
technical drawings,
test reports.

Commercial evidence

The supplier may provide:

customer references,
case studies,
delivery performance,
service network information,
warranty records,
implementation examples.

Independent confirmation

Depending on the product, useful evidence may include:

accredited laboratory reports,
certification body records,
external product databases,
verified reviews,
industry approvals,
partner confirmations.

Evidence graph

The preferred structure is not a folder of documents.

It is a relationship model:

PRODUCT
→ VERSION
→ CLAIM OR PARAMETER
→ EVIDENCE
→ ISSUER
→ DATE
→ VALIDITY
→ MARKET OR APPLICATION

Typical Trustworthiness failures

a certificate applies to the company but is presented as a product certificate,
a TDS has no date or version,
product claims cannot be linked to a test report,
documentation refers to an older version,
distributor descriptions conflict with manufacturer information,
case studies omit conditions and limitations,
regulatory claims are made without evidence.

Desired outcome

The agent can verify important claims and assign an appropriate level of confidence to the product and offer.

5. Executable

Can the agent perform a business action?

A product may be visible, understandable and credible but still not be usable in agentic commerce.

Executability means that the agent can move from information to action.

The agent should be able to:

check availability,
retrieve a document,
calculate a price or cost,
create a Direct RFQ,
receive a quotation,
accept or reject an offer,
create an order,
check order status,
request service,
track delivery.

Key assessment questions

Can the agent access current stock information?
Can it retrieve the correct technical document?
Can it calculate freight or TCO?
Can it create a structured RFQ?
Can it receive a machine-readable quotation?
Can it identify whether the quotation is indicative or binding?
Can it place an order within its authority?
Can it check fulfilment status?
Can it submit a service request?
Are actions available through API, MCP or A2A?

Executable components

Data access

The agent may need:

a product feed,
product API,
document API,
availability API,
compatibility service.

RFQ execution

The system should support:

RFQ creation,
validation,
clarification,
quotation generation,
quotation status,
quotation acceptance.

Transaction execution

Advanced functionality may include:

order creation,
stock reservation,
payment authorization,
delivery scheduling,
order tracking.

Agent interfaces

Possible interfaces include:

product feed,
REST API,
MCP server,
A2A Agent Card,
Direct RFQ endpoint,
webhook,
order-status service.

Typical Executability failures

stock information exists only by phone,
the agent can find a product but cannot request a quote,
documents require manual email exchange,
the RFQ form produces an unstructured message,
the quote is returned only as a PDF,
order status is unavailable outside the ERP,
no distinction exists between a calculation and a binding offer,
every action requires a human to re-enter data.

Desired outcome

The agent can safely complete defined commercial tasks without unnecessary manual re-entry.

6. Governed

Does the company control what the agent may do?

Governance distinguishes professional agentic commerce from uncontrolled automation.

An executable product without governance creates commercial, legal, operational and security risk.

The company must control:

permissions,
transaction limits,
contractual pricing,
confidential information,
approval workflows,
logs,
errors,
high-risk actions,
human escalation.

Key assessment questions

Is the buyer or agent authenticated?
Can the system verify which organization the agent represents?
Are public, partner, customer and confidential data separated?
Can the agent access only the information required for its task?
Are contract prices protected?
Are minimum margin and maximum discount enforced?
Are transaction-value limits defined?
Are human approval thresholds in place?
Are all actions logged?
Can an unusual transaction be stopped automatically?
Can a human take over the process?

Governed components

Identity and access

The system should control:

user identity,
agent identity,
company identity,
delegated authority,
role,
scope,
session validity.

Commercial guardrails

Rules may include:

minimum margin,
maximum discount,
maximum order value,
approved customer status,
credit limit,
permitted region,
allowed payment terms,
available stock reservation.

Data governance

Information should be divided into access layers:

public,
partner,
customer-specific,
confidential,
restricted.

Human-in-the-loop

Human approval should be required for:

high-value transactions,
new suppliers or buyers,
technical deviations,
uncertain substitutes,
missing evidence,
contract changes,
regulated or safety-critical products.

Observability and audit

The system should record:

who initiated the action,
which agent acted,
what data was used,
which rules were applied,
what tools were called,
what decision was made,
whether a human approved it.

Error and risk management

Required safeguards may include:

circuit breakers,
rate limits,
duplicate transaction protection,
idempotency keys,
anomaly detection,
rollback procedures,
manual override.

Typical Governance failures

an agent can access every customer’s price,
discount policy is stored only in prompts,
a language model can override the pricing engine,
there is no transaction-value limit,
no audit log exists,
product documents are available without access controls,
unusual order volumes do not trigger review,
the organization cannot explain why the agent made a decision.

Desired outcome

The agent can act only within clearly defined authority, business rules and risk boundaries.

The FUCTEG Scoring Model

Each FUCTEG dimension is scored from 0 to 5.

Scoring scale

0 — Non-existent

The required capability does not exist.

1 — Fragmented

Some information or functionality exists, but it is incomplete, inconsistent or manually accessible.

2 — Basic

The company has a usable foundation, but important gaps remain.

3 — Structured

Data and processes are defined, standardized and generally reliable.

4 — Agent-ready

The capability is machine-readable, integrated and usable by an agent.

5 — Agent-executable

The capability is real-time, verifiable, operational and governed for autonomous or semi-autonomous use.

Recommended FUCTEG Weights

Dimension	Weight
Findable	15%
Understandable	20%
Comparable	15%
Trustworthy	20%
Executable	20%
Governed	10%
Total	100%

The weighting reflects the fact that visibility alone is not enough.

A product that can be found but cannot be understood, verified or used is not truly agent-ready.

Calculation formula

Each area is scored from 0 to 5.

The normalized score for each area is:

Area Score = Rating ÷ 5 × Area Weight

The overall A2O score is:

A2O Score =
Findable Score
+ Understandable Score
+ Comparable Score
+ Trustworthy Score
+ Executable Score
+ Governed Score

Example

Assume a product receives:

Dimension	Rating	Weight	Weighted result
Findable	4/5	15%	12%
Understandable	3/5	20%	12%
Comparable	2/5	15%	6%
Trustworthy	4/5	20%	16%
Executable	2/5	20%	8%
Governed	3/5	10%	6%
Total			60%

The product reaches 60%, meaning it is agent-readable but not yet fully agent-ready.

Its main gaps are:

weak comparability,
limited transaction functionality,
insufficiently structured commercial execution.

A2O Maturity Levels

0–20%: Agent-Invisible

The product is effectively invisible or unusable for AI agents.

Typical characteristics:

information is scattered,
no stable identifiers exist,
documentation is difficult to find,
no structured commercial process exists,
agent access is unavailable.

The product may still be sold through existing human relationships, but it cannot reliably participate in agent-mediated discovery or procurement.

21–40%: Discoverable but Difficult to Qualify

The agent can find the product, but it cannot confidently determine whether the offer matches the buyer’s need.

Typical characteristics:

a product page exists,
basic descriptions are available,
some documents can be found,
technical parameters are incomplete,
commercial conditions require manual clarification,
compatibility and evidence are weak.

The product is visible, but qualification remains heavily dependent on human intervention.

41–60%: Agent-Readable

The product is represented in a structured and generally understandable form.

Typical characteristics:

identity is clear,
core parameters are structured,
classification exists,
documents are linked,
RFQ requirements are described,
some data is available through feeds or APIs.

The agent can interpret and shortlist the product, but may not be able to complete a quotation or transaction.

61–80%: Agent-Ready

The offer is prepared for reliable use in AI-assisted purchasing and Direct RFQ.

Typical characteristics:

product data is structured,
offers are comparable,
evidence is current,
compatibility is defined,
stock or lead time can be checked,
structured RFQ can be created,
quotation workflows are integrated,
governance rules are active.

The product can participate in agentic sourcing with human oversight.

81–100%: Agent-Executable

The product and offer can support controlled autonomous transactions.

Typical characteristics:

real-time data is available,
quotations can be created automatically,
agents can negotiate permitted variables,
orders can be generated,
status can be tracked,
actions are authenticated and logged,
hard commercial guardrails are enforced,
high-risk exceptions are escalated.

The product can operate inside a closed-loop Agentic Commerce process.

FUCTEG Readiness Audit

A practical FUCTEG audit should examine four layers.

Layer 1: Product data

identity,
classification,
parameters,
variants,
relationships,
documents.

Layer 2: Commercial offer

pricing,
MOQ,
quantity tiers,
availability,
delivery,
payment,
warranty,
service.

Layer 3: Agent interfaces

feed,
API,
MCP tools,
A2A Agent Card,
RFQ workflow,
webhooks,
order status.

Layer 4: Governance

access controls,
pricing rules,
transaction limits,
approvals,
logs,
exception handling,
human override.

The audit should not ask only whether a field exists.

It should ask whether the information is:

current,
structured,
consistent,
verifiable,
accessible,
actionable,
governed.

FUCTEG and Direct RFQ

The FUCTEG framework maps directly to the Direct RFQ process.

Findable

The buyer’s agent discovers:

the supplier,
the product,
the RFQ endpoint,
the required capability.

Understandable

The agent interprets:

product identity,
technical requirements,
variants,
constraints.

Comparable

The agent evaluates:

price,
performance,
delivery,
TCO,
warranty,
alternatives.

Trustworthy

The agent verifies:

documentation,
certificates,
test evidence,
product version,
supplier credibility.

Executable

The agent:

checks availability,
creates RFQ,
receives a quote,
accepts an offer,
creates an order,
tracks fulfilment.

Governed

The organization controls:

authority,
access,
price,
risk,
approval,
auditability.

This creates the complete A2O transaction chain:

Find → Understand → Compare → Verify → Execute → Govern

Strategic Meaning of the FUCTEG Model

The FUCTEG framework shows that Agent-Ready Offer Optimization is not a marketing-only activity.

It connects:

SEO and AEO,
product information management,
technical documentation,
e-commerce,
procurement,
CPQ,
ERP,
compliance,
cybersecurity,
AI governance.

It also changes the definition of a product page.

A traditional product page says:

This is our product.

An agent-ready product environment says:

This is the product’s verified identity, structured specification, commercial context, evidence, permitted actions and governance model.

The competitive advantage will not belong only to companies with the best products.

It will increasingly belong to companies whose products are easiest for trusted agents to:

discover,
understand,
compare,
verify,
purchase,
manage safely.

That is the role of A2O.

A2O prepares the offer.

FUCTEG measures its readiness.

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