---
id: "nr-b13c0471-agent-scale-starts-with-shared-data-and-governed-execution"
slug: "agent-scale-starts-with-shared-data-and-governed-execution"
title: "Agent Scale Starts with Shared Data and Governed Execution"
observed_at: "2026-09-13T11:14:17.693Z"
record_date: "2026-09-13"
date_kind: "observed_at"
novelty: "new"
source_platform: "mckinsey.com"
topics: ["agents","governance"]
entities: ["McKinsey"]
related_patterns: []
source_url: "https://mckinsey.com/capabilities/mckinsey-technology/our-insights/building-the-foundations-for-agentic-ai-at-scale"
source_urls: ["https://mckinsey.com/capabilities/mckinsey-technology/our-insights/building-the-foundations-for-agentic-ai-at-scale","https://www.mckinsey.com/~/media/mckinsey/business%20functions/technology/our%20insights/building%20the%20foundations%20for%20agentic%20ai%20at%20scale/building-the-foundations-for-agentic-ai-at-scale_vf.pdf"]
basket: {"id":"0a002c08-692d-4485-b9dc-0ffc04cebcca","revision":3,"review_ref":"b13c0471","cluster_id":"3255ef05-06d4-4286-a5de-ffe391649139","mention_count":1}
type: "post"
description: "McKinsey's architecture places semantic consistency, data lineage, and reusable data products ahead of uncontrolled agent proliferation."
why_it_matters: "Architecture teams should treat semantic contracts, lineage, ownership, and execution policy as reusable platform capabilities tied to priority workflows. The evidence is McKinsey's management and technology framework; reported client outcomes should not be treated as universal benchmarks. Watch whether organizations can reuse the same governed context across multiple agents without increasing exceptions or reconciliation work."
verification_level: "source-inspected-firecrawl"
signal_type: "field_note"
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:agent-scale-starts-with-shared-data-and-governed-execution"
retrieval_nugget: "McKinsey's architecture places semantic consistency, data lineage, and reusable data products ahead of uncontrolled agent proliferation. Enterprise agents need a shared data foundation and governed execution layer before they can coordinate reliable end-to-end workflows. This matters now because agent builders can create local automation faster than enterprises can reconcile conflicting definitions, permissions, and data quality. McKinsey recommends scaling a limited"
status: "published"
visuals: []
editorial_provenance: {"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:b47a65058dd8ac44c5acc044075276216e8162207a884aa4d3d443ae3359c3bf","reviewed_at":"2026-09-13T11:14:17.693Z","source_evidence_count":1,"verified_claim_count":2,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":2,"implication_count":1,"watch_condition_count":1,"related_record_count":0}
analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Enterprise agents need a shared data foundation and governed execution layer before they can coordinate reliable end-to-end workflows.","observed_facts":[{"text":"McKinsey recommends scaling a limited number of end-to-end workflows instead of multiplying disconnected point agents.","source_urls":["https://mckinsey.com/capabilities/mckinsey-technology/our-insights/building-the-foundations-for-agentic-ai-at-scale"]},{"text":"The article connects reusable data products, semantic consistency, lineage, and governed execution with reliable agent operation.","source_urls":["https://mckinsey.com/capabilities/mckinsey-technology/our-insights/building-the-foundations-for-agentic-ai-at-scale"]}],"mechanism":"The mechanism is a common context layer that resolves business meaning, traces data origin, supplies reusable interfaces, and constrains how agents act across systems of record.","why_now":"This matters now because agent builders can create local automation faster than enterprises can reconcile conflicting definitions, permissions, and data quality.","implications":["Architecture teams should treat semantic contracts, lineage, ownership, and execution policy as reusable platform capabilities tied to priority workflows."],"evidence_boundary":"The evidence is McKinsey's management and technology framework; reported client outcomes should not be treated as universal benchmarks.","watch_conditions":["Watch whether organizations can reuse the same governed context across multiple agents without increasing exceptions or reconciliation work."],"related_records":[],"new_branch_reason":"No existing New Runtime analytical record covers this exact management and technical mechanism closely enough."}
routes: {"html":"https://newruntime.com/posts/agent-scale-starts-with-shared-data-and-governed-execution/","markdown":"https://newruntime.com/posts/agent-scale-starts-with-shared-data-and-governed-execution.md","json":"https://newruntime.com/posts/agent-scale-starts-with-shared-data-and-governed-execution.json"}
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
---

# Agent Scale Starts with Shared Data and Governed Execution

## Retrieval answer

McKinsey's architecture places semantic consistency, data lineage, and reusable data products ahead of uncontrolled agent proliferation. Enterprise agents need a shared data foundation and governed execution layer before they can coordinate reliable end-to-end workflows. This matters now because agent builders can create local automation faster than enterprises can reconcile conflicting definitions, permissions, and data quality. McKinsey recommends scaling a limited

Enterprise agents need a shared data foundation and governed execution layer before they can coordinate reliable end-to-end workflows. This matters now because agent builders can create local automation faster than enterprises can reconcile conflicting definitions, permissions, and data quality.

McKinsey recommends scaling a limited number of end-to-end workflows instead of multiplying disconnected point agents. The article connects reusable data products, semantic consistency, lineage, and governed execution with reliable agent operation. The mechanism is a common context layer that resolves business meaning, traces data origin, supplies reusable interfaces, and constrains how agents act across systems of record.

Architecture teams should treat semantic contracts, lineage, ownership, and execution policy as reusable platform capabilities tied to priority workflows. The evidence is McKinsey's management and technology framework; reported client outcomes should not be treated as universal benchmarks. Watch whether organizations can reuse the same governed context across multiple agents without increasing exceptions or reconciliation work. The official companion presentation or report deck is available here: [view the presentation](https://www.mckinsey.com/~/media/mckinsey/business%20functions/technology/our%20insights/building%20the%20foundations%20for%20agentic%20ai%20at%20scale/building-the-foundations-for-agentic-ai-at-scale_vf.pdf).

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

## Continue in the graph

Next records for an agent, linked to their Markdown twins. Replace `.md` with `.json` for structured fields, including `next_reads`.

- topic hub: [Agents](https://newruntime.com/topics/agents.md): Explore the agents topic hub.
- topic hub: [Governance](https://newruntime.com/topics/governance.md): Explore the governance topic hub.
- collection: [Field Notes](https://newruntime.com/posts.json): Open the latest editorial analysis.
- collection: [Source Ledger](https://newruntime.com/source-ledger.json): Inspect the public source evidence graph.
- whole graph: [discovery graph](https://newruntime.com/discovery-graph.json) (typed nodes and edges) and [site index](https://newruntime.com/site-index.md)