Direct RFQ in Agentic Commerce: From Digital Forms to Machine-to-Machine Transactions
Most B2B companies still treat a request for quotation as a document.
A buyer prepares a specification, sends an email or PDF, and waits for the supplier to check availability, calculate pricing, confirm compliance and prepare an offer.
Direct RFQ introduces a different transaction architecture.
It turns the RFQ into a structured, machine-readable business object that can be exchanged directly between the buyer’s systems and the supplier’s systems—or between their respective AI agents.
This is where Direct RFQ connects with Agentic Commerce, A2A, MCP and Agent-Ready Offer Optimization.
What is Direct RFQ?
Direct RFQ is a private, structured request for quotation sent directly to a selected and verified supplier.
Unlike a broadcast RFQ published on an open marketplace, Direct RFQ is normally based on an existing or intentionally selected business relationship.
The request may contain:
- buyer and supplier identifiers,
- SKU, MPN, GTIN or technical specification,
- quantity and unit of measure,
- required delivery date,
- delivery location and Incoterms,
- acceptable substitutes,
- quality and compliance requirements,
- target price or budget constraints,
- required certificates and documentation,
- negotiation limits,
- response deadline.
The supplier’s systems can use this information to check inventory, production capacity, customer-specific conditions, logistics, credit limits and pricing policies.
The result is not merely a faster email response. It is a machine-generated, policy-compliant and auditable quotation.
Direct RFQ and Agentic Commerce
Agentic Commerce describes a commercial environment in which AI agents can perform parts of the buying and selling process on behalf of organizations.
A procurement agent may:
- detect a purchasing need in ERP or MRP,
- collect the required technical and commercial context,
- identify approved suppliers,
- submit a Direct RFQ,
- compare quotations,
- negotiate within authorized limits,
- recommend or execute an award,
- generate a purchase order.
On the supplier side, a sales agent may:
- authenticate the buyer,
- identify the requested product or configuration,
- check stock and capacity,
- call pricing and logistics systems,
- verify compliance requirements,
- generate a quotation,
- propose alternatives,
- escalate exceptions to a human specialist.
Direct RFQ is therefore one of the most important transaction primitives of Agentic Commerce.
Without a structured RFQ, an agent may understand purchasing intent but cannot reliably convert that intent into an executable B2B transaction.
The role of A2A
A2A, or Agent-to-Agent communication, enables agents from different organizations and technology environments to collaborate.
A procurement agent running in one company may need to communicate with a sales agent operating in another company’s CRM, ERP or commerce environment.
A2A can support:
- agent discovery,
- capability declaration,
- secure task exchange,
- long-running workflows,
- structured responses,
- status updates,
- human approval checkpoints.
In a Direct RFQ process, A2A can provide the communication layer through which the buyer’s agent asks the supplier’s agent to perform a specific business task:
Evaluate this requirement and return a valid commercial quotation.
A2A does not calculate the price itself. It coordinates the work between agents and the systems they represent.
The role of MCP
MCP, or Model Context Protocol, provides agents with controlled access to business data and tools.
A supplier’s agent may use MCP-compatible tools to:
- query inventory,
- retrieve product data,
- check customer-specific pricing,
- calculate freight,
- access technical documentation,
- validate certificates,
- create a quote in CPQ,
- register the opportunity in CRM,
- retrieve order status.
The distinction is important:
- A2A connects agents to other agents.
- MCP connects agents to data, tools and enterprise systems.
A Direct RFQ architecture may use both.
The buyer’s agent communicates with the supplier’s agent through A2A, while the supplier’s agent uses MCP or APIs to access ERP, PIM, WMS, CPQ and document repositories.
What is Agent-Ready Offer Optimization?
We use Agent-Ready Offer Optimization to describe the process of preparing a B2B product and commercial offer so that AI agents can reliably find, understand, compare, verify and execute it.
An agent-ready offer is not just a product page.
It includes:
- consistent product identifiers,
- structured technical attributes,
- clear units and tolerances,
- pricing logic,
- quantity breaks,
- availability,
- lead times,
- delivery rules,
- compatibility data,
- substitute relationships,
- compliance evidence,
- warranty and service conditions,
- RFQ requirements,
- machine-readable policies,
- API or agent-accessible actions.
A human buyer may tolerate an incomplete page and call the supplier for clarification.
An AI agent needs explicit data, predictable rules and verifiable evidence.
This is why Agent-Ready Offer Optimization becomes a prerequisite for Direct RFQ.
The Direct RFQ architecture
A mature process may look like this:
ERP or MRP trigger
A stock shortage, production order, maintenance alert or new project creates purchasing intent.
Buyer agent
The agent gathers specifications, quantities, deadlines, policies and compliance requirements.
A2A routing
The request is sent to a verified supplier agent with the required capabilities.
Supplier agent
The agent interprets the RFQ and calls approved enterprise tools.
MCP and API layer
ERP, PIM, CPQ, WMS, TMS, CRM and document systems provide validated data.
Deterministic pricing engine
Code-based rules calculate price, margin, discounts, freight and risk.
Quotation response
The supplier returns a structured offer with price, lead time, validity, deviations and supporting documents.
Bounded negotiation
Agents may negotiate selected variables within predefined guardrails.
Human approval or automatic award
Standard transactions may proceed automatically. Exceptions are escalated.
PO and sales order creation
The accepted offer is converted into corresponding records in the buyer’s and supplier’s systems.
When Direct RFQ creates the most value
Direct RFQ is particularly effective when:
- the buyer and supplier are already verified,
- products have structured technical specifications,
- quotations are frequent or repetitive,
- pricing depends on quantity, availability or delivery date,
- customer-specific conditions apply,
- response time affects operational continuity,
- procurement teams manage high volumes of spot buys,
- sales teams repeatedly process similar enquiries,
- a standard e-commerce checkout is too simple,
- a traditional engineering quotation is too slow.
Strong use cases include:
- MRO and spare parts,
- packaging materials,
- industrial components,
- chemicals and ingredients,
- contract manufacturing,
- freight and logistics,
- electronics,
- metals and commodities,
- configurable equipment,
- machine service and retrofit projects.
Why Direct RFQ matters
It reduces transaction latency
The objective is not necessarily a response in milliseconds.
The real KPI is Time-to-Valid-Quote: the time required to produce a complete, executable and policy-compliant offer.
It improves data quality
Structured RFQs reduce missing parameters, incorrect quantities, unclear delivery conditions and incompatible specifications.
It protects private commercial relationships
Customer-specific pricing and negotiated terms can remain inside a trusted one-to-one channel.
It enables dynamic quoting
The supplier can include current inventory, capacity, material costs, freight, credit risk and contractual conditions.
It reduces administrative work
Procurement and sales teams spend less time copying data between emails, spreadsheets, PDFs and ERP systems.
It creates the transaction layer for Agentic Commerce
Discovery alone is not commerce.
An agent may recommend a supplier, but Direct RFQ allows it to obtain a valid, personalized and actionable offer.
Guardrails are essential
Direct RFQ should never depend on an LLM inventing a price or commercial condition.
Critical decisions must remain governed by deterministic rules, including:
- minimum margin,
- maximum discount,
- approved customer status,
- credit limits,
- maximum transaction value,
- allowed countries,
- compliance requirements,
- inventory reservation limits,
- human approval thresholds.
Circuit breakers should stop the process when:
- the order value is unusually high,
- the quantity differs significantly from historical demand,
- required documents are missing,
- a substitute is technically uncertain,
- the requested discount exceeds policy,
- the buyer cannot be authenticated,
- the agent attempts to access unauthorized information.
The preferred operating model is:
zero-touch for routine transactions, human control for exceptions.
How companies should begin
A company does not need to deploy fully autonomous agents on day one.
The practical sequence is:
- standardize RFQ data,
- define mandatory quotation fields,
- clean product identifiers and attributes,
- connect product data, stock, pricing and documents,
- build structured RFQ intake,
- automate repeatable quotations,
- introduce deterministic pricing guardrails,
- expose selected tools through secure APIs or MCP,
- add A2A communication,
- expand autonomy only after monitoring real outcomes.
Direct RFQ is not the end of relationship-based B2B sales.
It is the automation of the repetitive transaction layer surrounding those relationships.
The future B2B sales process may not begin when a buyer visits a website or sends an email.
It may begin when a procurement agent detects a need, identifies a trusted supplier agent and asks:
Can you supply this specification, in this quantity, under these conditions—and return a valid, verifiable and executable offer?
Direct RFQ Agentic Commerce
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