“The competitive advantage is not the AI model — it is the learning loop an organization builds and owns.” — Satya Nadella, CEO, Microsoft
Enterprise Reasoning: An Open Standard for Enterprise AI
Turning collaborative human and AI reasoning into a durable enterprise asset
As enterprise AI expands from individual models to AI agents and decision-making workflows, reasoning cannot stay trapped inside agentic chats. Enterprise Reasoning is an open semantic standard for capturing reasoning in a structured form that software, humans, and AI can understand — so it can be preserved, governed, reused, and continuously improved.
Reasoning becomes a reusable artifact instead of disposable chat.
Capture expert decisions, assumptions, and rationale during reasoning — not only after AI produces an answer.
Manage reasoning like software: versions, approvals, promotion, rollback, and reuse.
Business definitions evolve with the organization and remain linked to reasoning.
Trace decisions back to reasoning, evidence, models, humans, and outcomes.
Reason across databases, documents, applications, APIs, and knowledge repositories without requiring centralized data first.
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.
- 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
- 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
From Enterprise AI 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.
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.
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.
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.
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.
- The recurring decision“Which wholesalers do we terminate this quarter?” — a fixed, repeatable decision-intelligence workflow.
- What changesEach quarter the margins, product mix, customers, and rules differ — so the reasoning is different every time.
- Mathematical Inferencing stepAn AI model analyzes gross margins across wholesalers and produces the reasoning and a recommended result.
- The standardEnterprise Reasoning captures that reasoning, its evidence, and the result in one deterministic, governed format.
- Deterministic stepThe same tool — and the humans reviewing it — consume the result identically, and can ask “is this reasoning correct?” every quarter.
- The outcomeConsistent, auditable, portable decisions despite changing inputs — with no reasoning logic re-implemented per tool or per model.
{
"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"
]
}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.
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 / approach | What it standardizes |
|---|---|
| MCP | Tool and resource access for models |
| A2A | Communication between agents |
| RAG | Retrieval of data and context into a model |
| Model prompting | Instructions to a single, specific model |
| Enterprise Reasoning | Reasoning models, evidence, assumptions, traceability, and governance — portable across systems |
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.
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.
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.
In-house decision workflows read the same format, so teams stop re-implementing how to interpret model output in every application.
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.
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.
- 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
- 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.
Participate
Everyone is welcome. Follow our progress or help shape the Enterprise Reasoning open standard.
Enterprise Reasoning in the news
Press release EIN Presswire
LangGrant Launches First Open Standard Initiative for a Safe Enterprise Learning Loop Built on Shared Human-AI Reasoning
Read press release
In the news Techstrong.ai
LangGrant Launches Initiative to Create AI Reasoning Standards
Read article
In the news I Programmer
Introducing The Enterprise Reasoning Initiative
Read article
In the news SD Times
LangGrant Launches the Industry’s First Open Standards Initiative for a Safe Enterprise
Read articleIn the news Techstrong.ai
Governing the Why: Enterprise Reasoning for Autonomous Agent Operations
Read articleWhite paper authors
Bob Kruger
Bob Kruger is the Chief Product Officer at Almaden, Inc where he is responsible for the creation and delivery of Almaden’s industry recognized Digital Employee Experience Management (DEX) solution, Collective IQ®.
Bob's early career included 11 years at Microsoft, where he led the product organization for systems management products and the creation of the Web-Based Enterprise Management and Common Information Model industry standards. This followed successful leadership of Microsoft’s standards efforts in networking, character encoding, object technology, application interoperability, and APIs. Bob helped steer Microsoft’s server and operating systems divisions toward interoperability and coexistence with other technologies, helping lay the groundwork for Microsoft’s enterprise success. He was a contributor and technical reviewer for Bill Gates' second book, Business @ the Speed of Thought.
After Microsoft, Bob was the Vice president of BMC software for e-business solutions and then Chief Technology Officer for Citrix systems. He also was the Vice President of StreamOne at Tech Data Corporation, driving innovation and development of the global platform that powered Tech Data Corporation’s cloud marketplace and integration technologies. At Tech Data he enabled significant global revenue growth by creation of a vibrant, startup-like product organization.
Paul Stanton
Paul Stanton is currently Vice President and co-founder of Windocks, LangGrant, and most recently EnterpriseReasoning.org. He has led product management with patented innovations including the first Docker containers for Windows SQL Server, and the first writable Windows based SQL Server database snapshotting, and other innovations.
He spent 8 years at Microsoft, as lead product manager for Windows internetworking, and established strategic relationships with Cisco Systems and Bay Networks, and managed remote Windows protocol development with Citrix systems. As Director of Enterprise Marketing he establishing Windows NT as “enterprise ready” through marketing relationships with Hewlett Packard, and other recognized technology vendors.
After Microsoft, Paul has become a serial entrepreneur co-founding Windocks and LangGrant, and now is leading efforts to define and establish an open standard for representation of human/AI collaborative reasoning at EnterpriseReasoning.org
Ramesh Parameswaran
Ramesh Parameswaran is founder and CEO of LangGrant, an enterprise memory platform for AI that delivers executable, reusable decision intelligence from databases, human expertise, and discovered semantics. Ramesh began his career at Microsoft where he was one of the first few product managers for Windows. He became Microsoft's youngest general manager at the time leading the engineering organization for Internet Explorer for Unix. He also led interoperability and standards initiatives like Distributed COM across operating systems with the Open Group, the Windows Unix subsystem for certifying Windows as an open Unix system, and Windows virtual private network (VPN).
He went on to found AskMe, a top-100 website whose technology was deployed at Global 2000 enterprises like Procter & Gamble, Novartis, and Pratt & Whitney. He also founded Windocks, which built the world’s first Docker SQL Server containers on Windows, deployed at enterprises like Novartis, American Family Insurance, and Migros. He leads the Enterprise Reasoning open standard initiative, defining a vendor-neutral format for collaborative reasoning between humans and AI. A coastal sailing skipper and former ski patroller, he also serves on the board of The Sailing Foundation.
Greg Coquillo
Greg Coquillo builds the infrastructure behind AI and backs the founders putting it to work. At Microsoft, he leads GW-scale programs within Azure AI and High Performance Computing, delivering GPU infrastructure for frontier AI model providers. Previously, he led product initiatives at AWS to scale AI and machine learning network infrastructure.
As a General Partner at Leading Edge Ventures, Greg invests in early-stage AI companies solving real business problems. A two-time LinkedIn Top Voice with 236,000 followers, he shares practical insights shaped by building at scale, investing in startups, and working through the technical challenges behind AI
Subhadeep Chatterjee
Subhadeep Chatterjee is a technology executive with over 25 years of experience building and scaling AI, cloud, and enterprise platform businesses. Leveraging his extensive background in enterprise leadership, he currently advises technology startups on strategic growth and dedicates his time to supporting non-profit organizations.
Previously, Subhadeep served in enterprise sales leadership at Google, where he managed global accelerator and incubator partnerships with a specialized focus on cloud infrastructure and AI solutions. Holding a degree in Electrical Engineering from the University of Washington, he consistently bridges complex technical architectures with commercial execution. He is based in Seattle, Washington.
Vidya Subramanian
Vidya Subramanian’s career began in Microsoft’s Windows 2000 networking organization, where she worked on the implementation and validation of IrDA interoperability standards. That early experience shaped a career-long interest in how shared standards enable technologies, teams and ecosystems to operate reliably at scale.
She went on to lead quality strategy for early interoperability between drugstore.com’s online platform and Rite Aid’s pharmacy systems, then helped build and scale CI/CD, platform engineering and global operations at Expedia and Egencia. As the industry evolved, she founded DevOpsly to bring those practices to startups and enterprises and later led large-scale DevOps transformation initiatives at Accenture.
Across these roles, her work has progressed from standards-based interoperability to automated delivery, scalable operations, SRE and AI-driven decision intelligence. She contributes to Enterprise Reasoning from an SRE perspective, with a focus on transforming operational telemetry into structured, auditable context that can support trusted human and agentic decisions.
Dan Wright
Dan Wright is a senior technology executive who built and scaled some of the world's largest digital advertising and marketplace businesses. He has 15 years of continuous P&L ownership at Amazon and Coupang (NYSE: CPNG; Fortune 150; $34.5B in 2025 revenue), launched, turned around, and scaled three multi-billion-dollar businesses to market leadership while leading organizations of more than 2,000 people across product, engineering, and commercial functions. He led two of Coupang's most advanced AI-driven business units and possesses cross-functional depth at the intersection of commerce, AI, and digital advertising.
Companies supporting the initiative
- Causal Dynamic Labs
- Conflux
- DeepGraph
- GirardAI
- HMX.ai
- LangGrant
- LEIT Data
- Ngentix
- Proof Analytics
- Skyhook
- The Knowledge Graph Guys
Enterprise Reasoning and Enterprise AI: Common Questions
What is Enterprise Reasoning?
How does Enterprise Reasoning support enterprise AI?
Why isn’t MCP or A2A enough?
Why not just build it and open-source it?
Who uses Enterprise Reasoning, and how?
Is this AI governance?
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.
Get in touch
Enterprise Reasoning is an open initiative supported by LangGrant.
contact@enterprisereasoning.org