--- title: "Internal AI Agent Marketplace: 2026 Guide" canonical: https://wavect.io/blog/internal-ai-agent-marketplace/ language: en description: "Build an internal AI agent marketplace with company context, identity, approvals, evaluations, observability, lifecycle controls and a 90-day rollout." image: "https://wavect.io/img/blog/headers/header_internal-ai-agent-marketplace.png" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 14 min read · 13 Aug 2026 Last reviewed August 13, 2026 [**Next**](/blog/linux-for-ai-agents/) # Internal AI Agent Marketplace: A 2026 Build Guide for Enterprise Teams TL;DR An internal AI agent marketplace is a governed company catalog where employees discover and request approved agents for specific jobs. The storefront is only the visible layer. A production setup also needs a registry, distinct agent identities, permission-aware company context, versioned evaluations, risk-based approval, policy enforcement before tool calls, observability, cost per accepted outcome and retirement states. Start with one department and two or three repeated, measurable jobs. In 90 days, build the catalog and access path, certify the first agents, pilot with controlled groups, then scale only what earns retained use and continued trust. Employees are already building agents for research, proposals, support, finance and internal operations. The next problem is not how to create another agent. It is how colleagues find the right one, know whether it is approved, connect it to company context without bypassing permissions, and see who owns it when something changes. An internal AI agent marketplace solves that distribution problem. Done well, it is part catalog, part approval workflow and part operational control plane. Done badly, it is a page of links that makes shadow AI easier to find. This guide focuses on the infrastructure between those two outcomes and complements our broader [internal AI adoption playbook](/blog/internal-ai-adoption-2026/). ## What is an internal AI agent marketplace? **An internal AI agent marketplace is a governed company catalog where employees discover and request approved AI agents for specific jobs.** Each listing connects the user-facing description to company context, access rules, an accountable owner, evaluation evidence, usage data, cost and lifecycle status. Unlike a public directory, it distributes agents inside an existing trust boundary. | Layer | Primary user | Question it answers | | --- | --- | --- | | Public agent marketplace | Buyer or developer | What third-party agents can we evaluate or purchase? | | Internal agent marketplace | Employee and team lead | Which approved agent can help with this job? | | Agent registry and control plane | IT, security and platform teams | What exists, what can it access, who owns it and is it healthy? | The three layers can share metadata, but they are not interchangeable. A polished employee storefront without a registry cannot prove ownership or permissions. A registry without a usable storefront gives IT visibility but does not improve adoption. The valuable system connects both. ## Why did enterprise agent stores become a real infrastructure category in 2026? The category is emerging because agent creation has become decentralized while accountability has not. Microsoft describes employees across functions and technical skill levels creating agents, backed by embedded guardrails, IT oversight and employee education in its [internal account of governing agents at scale](https://www.microsoft.com/insidetrack/blog/governing-ai-agents-at-scale-lessons-from-our-journey-at-microsoft/). That is no longer a single innovation team publishing software for everyone else. Distribution is also moving into the tools where employees already work. Google Cloud's 2026 Agent Gallery design lets employees browse partner agents and request access while administrators retain deployment control, according to its [Gemini Enterprise marketplace announcement](https://cloud.google.com/blog/products/ai-machine-learning/partner-built-agents-available-in-gemini-enterprise). The interface may look like an app store, but the important mechanism is the request and approval path behind it. There are now public examples of companies building the same pattern for their own workforce. Tata Elxsi states in its 2025 to 2026 annual report that its internal AI agent marketplace curates production-ready agents and supports reuse across delivery, quality, IT, marketing, legal, HR and learning and development. The claim appears alongside infrastructure, guardrails and role-based training, not as a stand-alone tool launch in the company's [filed annual report](https://nsearchives.nseindia.com/annual_reports/AR_29320_TATAELXSI_2025_2026_A_8677225_01062026164247.pdf). Scale makes a registry unavoidable. Gartner forecasts that an average global Fortune 500 company could use more than 150,000 agents by 2028, while only 13 percent of surveyed organizations believe they have the right governance in place. A forecast is not a measured outcome, but Gartner's recommended response is concrete: centralized inventory, agent identity, permissions, lifecycle management, information governance and continuous monitoring in its [April 2026 agent-sprawl guidance](https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-identifies-six-steps-to-manage-artificial-intelligence-agent-sprawl). ## What architecture does an internal agent marketplace need? Think of the marketplace as an employee-facing view over a governed agent registry. The registry is the source of truth. Search, recommendations and team collections are projections of that record. The runtime does not have to be the same product as the marketplace, but every run should resolve back to a registered version. 1. **Catalog and discovery:** searchable listings by job, department, system, data sensitivity and approval status. Use task language such as "prepare a renewal brief," not framework language such as "LangGraph agent." 2. **Identity and access:** a distinct machine identity for each deployed agent, a named human sponsor, role-based user access and least-privilege tool permissions. The agent should never inherit every permission of the person who published it. 3. **Company context:** connectors to approved sources with source-level permissions preserved at retrieval time. Context is not one shared vector database that silently flattens SharePoint, Confluence, Drive and CRM access. 4. **Evaluation and certification:** task-specific test sets, tool-call checks, security tests, a risk tier and an approval record tied to a version. A green badge must expire when the prompt, model, tools or data boundary changes materially. 5. **Policy gateway:** enforcement before tool execution, including scope checks, data-loss controls, human approval for consequential writes, rate limits and sandboxing where appropriate. 6. **Observability and economics:** traces, success and escalation rates, latency, cost per accepted outcome, incident history and feedback. Token spend alone cannot tell you whether an agent saves work. 7. **Lifecycle management:** draft, review, approved, restricted, deprecated and retired states, plus an owner review date. Ownerless agents and stale context are operational debt. Microsoft's current Agent Store documentation exposes the same underlying concerns in a vendor-specific form: administrators can inspect publisher, capabilities, knowledge, actions, security, compliance, certification and activity before assigning an agent to users or groups. It also supports agents built by the organization and agents from external platforms. That makes the [Microsoft 365 Agent Store administration model](https://learn.microsoft.com/en-us/microsoft-365/copilot/copilot-agent-store) a useful requirements checklist even when you build on another stack. ## What should every agent listing contain? A marketplace listing is an operational contract, not marketing copy. If an employee cannot predict what the agent will read, change and return, the listing is incomplete. | Field | Example | Why it matters | | --- | --- | --- | | Job and boundary | Drafts a renewal brief, never sends it | Defines the useful outcome and the autonomy limit | | Owner and sponsor | Revenue Operations, named role | Creates an escalation and review path | | Inputs and data sources | CRM opportunities, approved call notes | Makes company context visible | | Actions and permissions | CRM read, document create, no email send | Lets users and reviewers understand impact | | Evaluation status | 92 of 100 test cases accepted, reviewed 4 Aug | Turns "approved" into inspectable evidence | | Cost and service level | Target under EUR 0.40 per accepted brief | Supports routing and retirement decisions | | Version and change log | v1.4, model and CRM tool updated | Prevents silent behavioral drift | | Feedback and incident link | Report incorrect output or unsafe action | Closes the operating loop | Preserving source permissions is the most frequently underestimated part of company context. Our guide to [permission-aware RAG across SharePoint, Confluence and Google Drive](/blog/rag-permissions-sharepoint-confluence-drive/) covers that data plane. For agents that call internal tools, the identity boundary belongs at the gateway, as described in our [enterprise MCP authorization architecture](/blog/enterprise-mcp-authorization-architecture/). ## How should the publish and approval workflow work? Use risk-based lanes instead of one approval queue. A read-only personal summarizer over a user's own documents should not wait behind an agent that can approve refunds or alter HR records. At the same time, "built internally" is not evidence that an agent is safe. 1. **Register:** the builder declares purpose, owner, users, data sources, tools, model, autonomy and expected value. 2. **Classify:** a ruleset assigns a provisional risk tier based on data sensitivity, write capability, external communication and decision impact. 3. **Test:** automated evals and security tests run against a versioned release candidate. High-impact cases add domain and legal review. 4. **Approve and scope:** reviewers publish to a defined group with time-bound permissions, budgets and human checkpoints. 5. **Observe:** the platform captures outcomes, denied actions, overrides, incidents, latency and cost. 6. **Review or retire:** material changes trigger re-evaluation. Unused, ownerless or consistently failing agents leave the store. Singapore's IMDA Model AI Governance Framework for Agentic AI provides a useful current baseline. It tells deployers to assess and bound risk upfront, keep humans meaningfully accountable, implement technical controls and testing, monitor after deployment and enable responsible use. It also recommends scoped, least-privilege and time-bound authorization. Those principles translate directly into marketplace gates in the [January 2026 agentic AI governance framework](https://www.imda.gov.sg/-/media/imda/files/about/emerging-tech-and-research/artificial-intelligence/mgf-for-agentic-ai.pdf). ## Should you buy a platform, extend your existing suite or build a custom marketplace? | Approach | Best when | Main trade-off | | --- | --- | --- | | Extend Microsoft, Google, Salesforce, ServiceNow or another suite | Most agents and employee identity already live in one ecosystem | Fast adoption, but catalog and governance become platform-shaped | | Use a dedicated agent registry or control plane | Agents span several runtimes and business systems | Better cross-platform visibility, with another operational component | | Build a thin internal marketplace | You need a tailored workflow, deployment model or regulated boundary | Exact fit and ownership, but you own lifecycle and integrations | | Start with a curated directory | You have fewer than roughly ten agents and need to prove demand | Cheap learning, but it must not be mistaken for runtime governance | Do not start the decision with storefront screenshots. Inventory the agents, identity providers, data systems, runtimes and approval obligations you already have. If one suite covers most of that graph, extend it. If not, keep a vendor-neutral registry and publish approved agents into the employee surfaces where work happens. Our [custom software versus off-the-shelf decision guide](/software-development-guide/custom-software-vs-off-the-shelf/) helps structure the ownership and lock-in side of that choice. ## What is a practical 90-day rollout? A useful first release is not a company-wide bazaar. It is a small catalog with a trustworthy publishing path and two or three agents that solve repeated work. 1. **Days 1 to 15, inventory and select:** discover existing agents and shadow workflows, choose one department, define the listing contract, nominate owners and select two low-to-medium-risk jobs with measurable volume. 2. **Days 16 to 35, build the registry:** implement SSO, role mapping, the catalog schema, search, access requests and basic lifecycle states. Connect to one employee surface rather than launching another destination nobody opens. 3. **Days 36 to 60, certify the first agents:** preserve source permissions, add evaluation sets, test tool boundaries, define human approvals and capture cost and outcome traces. Use our [agent evaluation and sandbox security checklist](/blog/ai-agent-eval-sandbox-security-checklist/) before publishing. 4. **Days 61 to 75, controlled pilot:** release to one or two groups, watch search failures, access denials, abandoned runs, overrides and support requests. Fix the workflow before adding inventory. 5. **Days 76 to 90, decide what scales:** publish operating metrics, retire weak agents, document the approval lane and open a repeatable submission process for the next teams. ## Which metrics show whether the marketplace creates value? - **Discovery success:** searches that lead to an appropriate agent, not just page views. - **Activation and retained use:** approved users who complete a first task and return for the same job. - **Accepted outcome rate:** outputs used without major rework, segmented by agent version. - **Human override and escalation rate:** a safety and workflow-fit signal, not automatically a failure. - **Cost per accepted outcome:** model, tool and platform cost divided by work employees actually keep. - **Reuse and consolidation:** teams served by shared agents and duplicate agents retired. - **Governance lead time:** time from submission to a risk-appropriate decision. These metrics prevent the catalog from optimizing for quantity. One dependable agent used weekly by a whole operations team is worth more than fifty demos with impressive descriptions and no retained use. ## When does an internal AI agent marketplace setup make commercial sense? The service makes sense when agents already exist across departments, employees cannot tell what is approved, security reviews repeat from scratch, or useful prototypes stall before distribution. It is too early when the company has no repeated agent use case, no process owner and no source systems ready for controlled access. Our [AI Enablement service](/services/ai-enablement/) can cover the inventory, architecture, marketplace MVP, context and identity integration, evaluation gates, observability and team handover. The first engagement should produce an owned system and a repeatable operating model, not dependency on another proprietary demo. If you are assessing whether the opportunity is mature enough, [book an internal AI workflow and agent-marketplace scoping call](/contact/?intent=operations). ## Frequently asked questions ### Is an internal agent store the same as an AI agent marketplace? In enterprise searches, the terms often overlap. "Store" emphasizes employee discovery and distribution. "Marketplace" can also imply external sellers, procurement or pricing. Define the product by its controls: internal catalog, approved publishers, scoped access, evaluation evidence, ownership and lifecycle management. ### Does every employee need a personal AI agent? No. Start with repeated jobs and shared agents owned by a function. Personal agents can help individuals, but the marketplace creates more leverage when a validated workflow captures company know-how and serves a group without widening data access. ### Can we use company context without copying all data into one AI database? Yes. Retrieve from source systems at run time, preserve their access controls, minimize indexed content and pass only the context required for the task. The marketplace should describe the connected sources, while the data plane enforces what each user and agent may retrieve. ### Is Microsoft 365 or Gemini Enterprise enough? It can be when most identity, data, agent building and employee work already live in that ecosystem. A multi-cloud or regulated organization may still need a vendor-neutral registry, policy gateway and observability layer, then publish approved agents into Microsoft 365, Gemini Enterprise or other employee surfaces. ### What should the first marketplace MVP include? SSO, a searchable listing schema, owner and version records, group-based access requests, evaluation status, two or three production agents, basic traces, cost reporting, feedback and retirement states. Recommendations, ratings and hundreds of listings can wait until discovery becomes the bottleneck. ## Final thoughts An internal AI agent marketplace is not primarily a storefront. It is the employee-facing edge of an agent operating model. The catalog helps people discover approved capabilities, while the registry, identity layer, permission-aware context, evaluations, policy gateway, observability and lifecycle controls make those capabilities safe enough to reuse. Start with two or three repeated jobs, publish evidence instead of badges, measure accepted outcomes instead of agent count, and retire what does not earn continued trust. ## You may also like.. [**How to roll out AI internally without shelfware** The broader adoption sequence around process selection, cost, compliance, implementation and handover.](/blog/internal-ai-adoption-2026/) [**AI Enablement vs an in-house AI hire** Compare external setup support with building the full internal capability before your agent marketplace is proven.](/compare/ai-enablement-vs-in-house-ai-hire/) Agent engineering ## Continue through this cluster Coding agents, MCP, context systems, evaluation and the controls required for dependable automation. [Start with the cornerstone**Graph Engineering for AI Agents: When Does a Knowledge Graph Pay Off?**](/blog/graph-engineering-ai-agents/) - [Is Linux the Best OS for AI Agents? A 2026 Infrastructure Guide](/blog/linux-for-ai-agents/) - [MCP Cloud vs Manufact Cloud: MCP Hosting Guide](/blog/mcp-cloud-vs-manufact-cloud/) - [How to Make AI Writing Sound Human with Agent Skills](/blog/ai-writing-agent-skills/) - [NVIDIA NOOA Review: Are Object-Oriented Agents Production-Ready?](/blog/nvidia-nooa-object-oriented-agents-review/) - [Strix AI Pentesting: 30-Day Pilot and Buying Guide for 2026](/blog/strix-ai-pentesting-pilot-guide-2026/) Inbox, without the noise ## Follow the work that matters to you Get a short email when we publish something new. Follow the whole blog or only the problems you care about. [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 14 min read · 13 Aug 2026 Last reviewed August 13, 2026 [**Next**](/blog/linux-for-ai-agents/) New posts by email × × Get new posts by email A short email when we publish. Free, no tracking. ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "founder": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "legalRepresentative": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "name": "Wavect GmbH", "subjectOf": { "@id": "https://wavect.io/verified-claims.json#dataset", "@type": "Dataset", "creator": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.", "inLanguage": "en", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "name": "Wavect verified publication claims", "url": "https://wavect.io/verified-claims.json" }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "sameAs": [ "https://www.wikidata.org/wiki/Q139796367", "https://www.linkedin.com/in/jocr77/", "https://github.com/jo-chris" ], "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/#website", "@type": "WebSite", "inLanguage": [ "en", "de", "es", "zh" ], "name": "Wavect", "potentialAction": { "@type": "SearchAction", "query-input": "required name=search_term_string", "target": { "@type": "EntryPoint", "urlTemplate": "https://wavect.io/search/?q={search_term_string}" } }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/internal-ai-agent-marketplace/#webpage", "@type": "WebPage", "dateModified": "2026-08-13", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-08-13", "url": "https://wavect.io/blog/internal-ai-agent-marketplace/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "An internal AI agent marketplace is a governed company catalog where employees discover and request approved agents for specific jobs. The storefront is only the visible layer. A production setup also needs a registry, distinct agent identities, permission-aware company context, versioned evaluations, risk-based approval, policy enforcement before tool calls, observability, cost per accepted outcome and retirement states. Start with one department and two or three repeated, measurable jobs. In 90 days, build the catalog and access path, certify the first agents, pilot with controlled groups, then scale only what earns retained use and continued trust.", "articleBody": " Blog overview/AI and agents/Agent engineering Internal AI Agent Marketplace: A 2026 Build Guide for Enterprise Teams TL;DR An internal AI agent marketplace is a governed company catalog where employees discover and request approved agents for specific jobs. The storefront is only the visible layer. A production setup also needs a registry, distinct agent identities, permission-aware company context, versioned evaluations, risk-based approval, policy enforcement before tool calls, observability, cost per accepted outcome and retirement states. Start with one department and two or three repeated, measurable jobs. In 90 days, build the catalog and access path, certify the first agents, pilot with controlled groups, then scale only what earns retained use and continued trust. Employees are already building agents for research, proposals, support, finance and internal operations. The next problem is not how to create another agent. It is how colleagues find the right one, know whether it is approved, connect it to company context without bypassing permissions, and see who owns it when something changes. An internal AI agent marketplace solves that distribution problem. Done well, it is part catalog, part approval workflow and part operational control plane. Done badly, it is a page of links that makes shadow AI easier to find. This guide focuses on the infrastructure between those two outcomes and complements our broader internal AI adoption playbook. What is an internal AI agent marketplace? An internal AI agent marketplace is a governed company catalog where employees discover and request approved AI agents for specific jobs. Each listing connects the user-facing description to company context, access rules, an accountable owner, evaluation evidence, usage data, cost and lifecycle status. Unlike a public directory, it distributes agents inside an existing trust boundary. LayerPrimary userQuestion it answers Public agent marketplaceBuyer or developerWhat third-party agents can we evaluate or purchase? Internal agent marketplaceEmployee and team leadWhich approved agent can help with this job? Agent registry and control planeIT, security and platform teamsWhat exists, what can it access, who owns it and is it healthy? The three layers can share metadata, but they are not interchangeable. A polished employee storefront without a registry cannot prove ownership or permissions. A registry without a usable storefront gives IT visibility but does not improve adoption. The valuable system connects both. Why did enterprise agent stores become a real infrastructure category in 2026? The category is emerging because agent creation has become decentralized while accountability has not. Microsoft describes employees across functions and technical skill levels creating agents, backed by embedded guardrails, IT oversight and employee education in its internal account of governing agents at scale. That is no longer a single innovation team publishing software for everyone else. Distribution is also moving into the tools where employees already work. Google Cloud's 2026 Agent Gallery design lets employees browse partner agents and request access while administrators retain deployment control, according to its Gemini Enterprise marketplace announcement. The interface may look like an app store, but the important mechanism is the request and approval path behind it. There are now public examples of companies building the same pattern for their own workforce. Tata Elxsi states in its 2025 to 2026 annual report that its internal AI agent marketplace curates production-ready agents and supports reuse across delivery, quality, IT, marketing, legal, HR and learning and development. The claim appears alongside infrastructure, guardrails and role-based training, not as a stand-alone tool launch in the company's filed annual report. Scale makes a registry unavoidable. Gartner forecasts that an average global Fortune 500 company could use more than 150,000 agents by 2028, while only 13 percent of surveyed organizations believe they have the right governance in place. A forecast is not a measured outcome, but Gartner's recommended response is concrete: centralized inventory, agent identity, permissions, lifecycle management, information governance and continuous monitoring in its April 2026 agent-sprawl guidance. What architecture does an internal agent marketplace need? Think of the marketplace as an employee-facing view over a governed agent registry. The registry is the source of truth. Search, recommendations and team collections are projections of that record. The runtime does not have to be the same product as the marketplace, but every run should resolve back to a registered version. Catalog and discovery: searchable listings by job, department, system, data sensitivity and approval status. Use task language such as \"prepare a renewal brief,\" not framework language such as \"LangGraph agent.\" Identity and access: a distinct machine", "articleSection": "Engineering", "author": { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/" }, "citation": [ { "@type": "WebPage", "name": "internal account of governing agents at scale", "url": "https://www.microsoft.com/insidetrack/blog/governing-ai-agents-at-scale-lessons-from-our-journey-at-microsoft/" }, { "@type": "WebPage", "name": "Gemini Enterprise marketplace announcement", "url": "https://cloud.google.com/blog/products/ai-machine-learning/partner-built-agents-available-in-gemini-enterprise" }, { "@type": "WebPage", "name": "filed annual report", "url": "https://nsearchives.nseindia.com/annual_reports/AR_29320_TATAELXSI_2025_2026_A_8677225_01062026164247.pdf" }, { "@type": "WebPage", "name": "April 2026 agent-sprawl guidance", "url": "https://www.gartner.com/en/newsroom/press-releases/2026-04-28-gartner-identifies-six-steps-to-manage-artificial-intelligence-agent-sprawl" }, { "@type": "WebPage", "name": "Microsoft 365 Agent Store administration model", "url": "https://learn.microsoft.com/en-us/microsoft-365/copilot/copilot-agent-store" }, { "@type": "WebPage", "name": "January 2026 agentic AI governance framework", "url": "https://www.imda.gov.sg/-/media/imda/files/about/emerging-tech-and-research/artificial-intelligence/mgf-for-agentic-ai.pdf" } ], "dateModified": "2026-08-13", "datePublished": "2026-08-13", "description": "An internal AI agent marketplace is a governed company catalog where employees discover and request approved agents for specific jobs. 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