Enterprise ReasoningAn open initiative Download white paper

“The competitive advantage is not the AI model — it is the learning loop an organization builds and owns.” — Satya Nadella, CEO, Microsoft

A Standard for Enterprise Reasoning in Decision Intelligence

Turning collaborative human and AI reasoning into a durable enterprise asset

As organizations adopt multiple AI models, agents, and human experts — reasoning cannot stay trapped inside agentic chats. Enterprise Reasoning is an open semantic standard for capturing it in a structured form software, humans, and AI can understand — so it can be preserved, governed, and continuously improved.

Structured Reasoning

Reasoning becomes a reusable artifact instead of disposable chat.

Human-AI Collaborative Reasoning

Capture expert decisions, assumptions, and rationale during reasoning — not only after AI produces an answer.

Reasoning Lifecycle

Manage reasoning like software: versions, approvals, promotion, rollback, and reuse.

Evolving Semantic Intelligence

Business definitions evolve with the organization and remain linked to reasoning.

Attribution

Trace decisions back to reasoning, evidence, models, humans, and outcomes.

Multi-Source Reasoning

Reason across databases, documents, applications, APIs, and knowledge repositories without requiring centralized data first.

See how it works
Why another AI standard?

AI is instantaneous, stateless inference. Decision making requires stateful reasoning over time.

AI models produce reasoning in unstructured chat form that cannot be used by deterministic software tools or collaborated upon in the organization. The decision-intelligence workflow and tools that run afterward need established structure. Today there is no standard format for those reasoning results, so every tool re-implements how to interpret model output each time! That is a standards problem.

Today, without a standard
  • No stable, shared format for reasoning results
  • Every tool re-implements how to read model output
  • Deterministic workflows can’t safely consume probabilistic output
  • Governance and audit break when the model changes
What Enterprise Reasoning standardizes
  • Representation of reasoning, evidence, and assumptions
  • A deterministic result format workflows can consume
  • Traceability so every decision can be audited
  • Governance applied consistently across tools
  • Portability — the same results, any model
How it works

From AI Mathematical Inference to Joint Human-AI Decision Making

A math inference system for reasoning is useful when the inputs change every cycle. Acting on it needs a deterministic system, because the workflow is fixed and must behave the same every time. Enterprise Reasoning is the standard result format in between — the handoff that lets deterministic tools and processes consume AI’s reasoning reliably.

Inferencing

An AI model produces the initial reasoning

Each cycle the data, customers, and rules differ, so a model reasons over the new inputs and reaches a result. This step is — and should be — inferencing.

Different every run
The standard

Enterprise Reasoning guides the inferencing and captures the results

The reasoning, evidence, and result are written into one standard, deterministic, governed format — the same shape every time, independent of the model that produced it.

Same format, any model
Compounding Reasoning

Reasoning becomes a reusable enterprise asset

Decision Intelligence tools, AI, and people act on the same structured reasoning objects — to decide, collaborate, report, and audit. Each cycle improves the reasoning asset – teams and AI build on organizational intelligence.

Structured reasoning compounds over time
In practice

An example: deciding which wholesalers to cut

A distributor reviews wholesale gross margins every quarter to decide which wholesalers to terminate. The decision workflow is the same each quarter — but the data, customers, and rules are not, so the reasoning must be produced fresh each time. Here is how Enterprise Reasoning lets the same deterministic tool and reviewers act on it identically.

  1. The recurring decision
    “Which wholesalers do we terminate this quarter?” — a fixed, repeatable decision-intelligence workflow.
  2. What changes
    Each quarter the margins, product mix, customers, and rules differ — so the reasoning is different every time.
  3. Mathematical Inferencing step
    An AI model analyzes gross margins across wholesalers and produces the reasoning and a recommended result.
  4. The standard
    Enterprise Reasoning captures that reasoning, its evidence, and the result in one deterministic, governed format.
  5. Deterministic step
    The same tool — and the humans reviewing it — consume the result identically, and can ask “is this reasoning correct?” every quarter.
  6. The outcome
    Consistent, auditable, portable decisions despite changing inputs — with no reasoning logic re-implemented per tool or per model.
The standard format — what step 4 produces wholesaler-review.2026-Q2.er.json 
{
  "er_version": "0.1",
  "plan": {
    "id": "wholesaler-termination-review",
    "decision": "Which wholesalers do we terminate this quarter?",
    "version": "4.2.0",
    "previous_version": "4.1.0",
    "stage": "production",
    "period": "2026-Q2"
  },
  "semantics": {
    "gross_margin_pct": {
      "definition": "(net_revenue - landed_cost) / net_revenue",
      "version": "3.1.0",
      "changed_in_plan": "4.2.0",
      "owner": "finance.controllership"
    },
    "strategic_account": {
      "definition": "wholesaler protected by sales leadership regardless of margin",
      "version": "1.0.0",
      "owner": "sales.ops"
    }
  },
  "evidence": [
    { "id": "ev-1", "source": "erp.sales_ledger", "scope": "2026-04-01 to 2026-06-30", "rows": 184213, "snapshot": "sha256-9f2c4a71b3" },
    { "id": "ev-2", "source": "crm.accounts", "rows": 412, "snapshot": "sha256-1d8406c095" }
  ],
  "reasoning": [
    {
      "step": 1,
      "produced_by": "model:vendor-x/reasoner-2.4",
      "claim": "27 wholesalers fell below the 12% gross-margin floor for two consecutive quarters.",
      "evidence": ["ev-1"],
      "confidence": 0.94
    },
    {
      "step": 2,
      "produced_by": "human:j.alvarez",
      "kind": "judgment",
      "applied_at": "reasoning-time",
      "claim": "Exclude the 6 wholesalers in hurricane-affected territories.",
      "rationale": "Territory disruption is temporary and is not a performance signal.",
      "affects": ["step-1"]
    },
    {
      "step": 3,
      "produced_by": "model:vendor-x/reasoner-2.4",
      "claim": "Of the remaining 21, 14 are below the floor on product mix alone and can be renegotiated rather than terminated.",
      "evidence": ["ev-1", "ev-2"],
      "confidence": 0.81
    }
  ],
  "result": {
    "terminate": 7,
    "renegotiate": 14,
    "accounts": [
      { "wholesaler_id": "W-10488", "gross_margin_pct": 6.4, "action": "terminate" },
      { "wholesaler_id": "W-33107", "gross_margin_pct": 9.7, "action": "renegotiate", "override": "strategic_account" }
    ]
  },
  "attribution": {
    "model_steps": 2,
    "human_steps": 1,
    "accountable_reviewer": "j.alvarez",
    "approved_on": "2026-07-14"
  },
  "diff_from_4.1.0": [
    "gross_margin_pct now nets landed cost (semantics 3.0.0 to 3.1.0)",
    "territory-disruption exclusion added as a reasoning-time judgment"
  ]
}
Illustrative and abbreviated — the point is the shape. Evidence, model reasoning, human judgment applied at reasoning-time, and the result travel together in one governed artifact — versioned, diffable, and attributable. Next quarter the numbers change; the shape does not, so the same tool and the same reviewers consume Q3 exactly as they consumed Q2.
Also applies to

Regulatory variance reporting. “Is our reasoning for the variances we report to the state correct?” A fixed periodic filing workflow consumes changing, AI-produced reasoning in one standard result format — so every filing is computed, reviewed, and audited the same way.

Where it fits

Necessary standards. One missing layer.

MCP standardizes tool access. A2A standardizes agent communication. RAG and prompting move context and data. Enterprise Reasoning standardizes the one thing none of them do: the representation, exchange, governance, and validation of reasoning itself.

Standard / approachWhat it standardizes
MCPTool and resource access for models
A2ACommunication between agents
RAGRetrieval of data and context into a model
Model promptingInstructions to a single, specific model
Enterprise ReasoningReasoning models, evidence, assumptions, traceability, and governance — portable across systems
Why it matters

Reasoning is a compounding enterprise asset

Competitive advantage

Build organizational intelligence that accumulates — an asset competitors cannot copy.

Collaboration

Share explainable reasoning across teams — and between humans and AI — with confidence.

Cost reduction

Reuse reasoning instead of regenerating it every time — cutting token and compute spend.

Contribution

Attribute decisions, revenue, and costs back to the reasoning that produced them.

Who uses it, and how

Built for the tools and people that act on reasoning

The standard is consumed by whatever runs after reasoning — the deterministic side of the handoff. Each consumer gets AI’s reasoning in the same governed shape every time, so their workflow logic never has to interpret probabilistic model output.

Software vendors’ decision tools

Vendors building decision-intelligence products consume a deterministic result format — so their workflow logic stays stable across models and customers instead of being rebuilt per integration.

Internal, org-built tools

In-house decision workflows read the same format, so teams stop re-implementing how to interpret model output in every application.

People

Analysts, operators, and reviewers who take the same deterministic action each cycle get results in the same shape every time — easy to trust, compare, and audit.

Who builds it

Who should help build the standard

Enterprise software vendors
AI platform providers
Standards organizations
Regulated industries & governments
Universities & researchers
Consultancies & enterprise architects
Our approach

Why an open standard — not just open source

A fair question: why not simply build Enterprise Reasoning and open-source it? We may well publish reference material. But code alone is not a standard — and a result format only works if the whole ecosystem can rely on it.

Publish & open-source alone
  • Becomes one vendor’s format, not a shared contract
  • Deterministic tools hesitate to build on a single-owner spec
  • No neutral governance for how it evolves
  • Forks and drift fragment interoperability
A group-backed open standard
  • Multi-party backing drives real adoption
  • Vendor-neutral governance tools can rely on
  • Shared semantics that stay stable as it evolves
  • Safe for vendors and enterprises to build on

In short: standards are adopted when a group backs them. That is why Enterprise Reasoning is an open specification — supported by LangGrant, and shaped by many.

Get involved

Members

Our members are a select group of leaders who believe Enterprise Reasoning should be an open standard — and are helping establish, author, and evangelize it for the greater good of the field.

To get involved, write to contact@enterprisereasoning.org

  • Shared belief
    Organizations — not models — should own enterprise reasoning.
  • Architectural leadership
    A proven record of supporting open, interoperable architectural principles.
  • Evangelism
    The ability and reach to help evangelize a new technology category.
  • Technical authorship
    Authoring white papers, reference architectures, and specifications.
  • Practical commitment
    A willingness to implement the framework, or parts of it, in practice.
Enterprise workspace
By invitation

A circle of members shaping an open standard for the AI era.

FAQ

Common questions

Why isn’t MCP or A2A enough?
MCP standardizes how models access tools and A2A standardizes how agents communicate — but neither defines a stable, governable format for the reasoning results that deterministic workflows consume afterward. Enterprise Reasoning standardizes that handoff, so tools and people can act on AI’s reasoning the same way every time.
Why not just build it and open-source it?
We may well publish reference material. But code alone is not a standard — adoption and vendor-neutral evolution need a group backing it. A single-owner format is one vendor’s spec; a group-backed open standard is something the whole ecosystem can safely build on.
Who uses Enterprise Reasoning, and how?
Deterministic decision-intelligence tools — from software vendors or built in-house — and the people who act on the results. They consume AI’s reasoning in one governed format, so their workflow logic never has to interpret probabilistic model output, and every decision can be audited.
Is this AI governance?
It’s the interoperability standard that makes AI governance practical: consistent representation, traceability, and auditability of reasoning across models, agents, and tools — the deterministic layer regulators and risk teams need.
The white paper

Read the Enterprise Reasoning white paper

Start with the 2-page executive brief, or request the full paper — the interoperability problem, how Enterprise Reasoning relates to MCP, A2A, and RAG, the framework it defines, and why the standard matters now.

  • The problem — and why it needs a standard now
  • Where Enterprise Reasoning fits alongside MCP and A2A
  • The framework and how to help shape it

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If reasoning interoperability matters to your organization, help shape the standard.

Start with the white paper — the problem, the framework, and how to get involved in defining Enterprise Reasoning.

Contact

Get in touch

Enterprise Reasoning is an open initiative supported by LangGrant.

contact@enterprisereasoning.org
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