AI Agent Marketplace for Enterprise (2026 Guide)
An enterprise can buy AI agents today from marketplaces run by Salesforce, Microsoft, Google, AWS, ServiceNow, Oracle, Workday, HubSpot and OpenAI. Each is good at one thing: finding an agent for a task inside that vendor's platform. None of them gives you an organisation of agents. Agents bought one at a time share no context, own no outcome and answer to no lead. That is why a shelf of siloed agents rarely passes the enterprise bar, and why agent teams do.
Which AI agent marketplaces matter for an enterprise?
We checked each marketplace against its own pages and announcements in October 2026. The list is in no particular order, and this is not a ranking. Products in this space change monthly, so confirm the details with the vendor.
Marketplace: Salesforce AgentExchange, Who publishes agents: Salesforce and vetted partners, Where the agents work: Inside Agentforce, on Salesforce data, How agents work together: MCP servers listed; A2A and MCP announced with Agentforce 3, Governance it offers: Einstein Trust Layer, audit trail, admin data boundaries
Marketplace: Microsoft Agent Store and Agent 365, Who publishes agents: Microsoft, partners and your own organisation, Where the agents work: Microsoft 365 Copilot, on Microsoft Graph data, How agents work together: Copilot Studio multi-agent; A2A and MCP in Foundry, Governance it offers: Agent registry, Entra Agent ID, conditional access, audit
Marketplace: Google Cloud Marketplace for Gemini Enterprise, Who publishes agents: Partners validated for Gemini Enterprise, Where the agents work: Gemini Enterprise, bought on your Google Cloud account, How agents work together: A2A Agent Card required to list, Governance it offers: IAM, private marketplace, admin approval
Marketplace: AWS Marketplace: AI Agents and Tools, Who publishes agents: AWS partners, Where the agents work: Your AWS account, through Bedrock AgentCore, How agents work together: Listings flag MCP and A2A support, Governance it offers: AgentCore identity and observability
Marketplace: ServiceNow Store, Who publishes agents: ServiceNow and partners, Where the agents work: The Now Platform, How agents work together: AI Agent Orchestrator and AI Agent Fabric, on A2A and MCP, Governance it offers: AI Control Tower, human approval in workflows
Marketplace: Oracle Fusion AI Agent Marketplace, Who publishes agents: Oracle partners, validated by Oracle, Where the agents work: Inside Oracle Fusion Applications, How agents work together: Agent teams, MCP, A2A agent cards, Governance it offers: Human approval steps, monitoring, tracing
Marketplace: Workday Agent Partner Network, Who publishes agents: Workday, partners and your own agents, Where the agents work: Workday HR and finance data, How agents work together: Agent Gateway for partner agents, Governance it offers: Agent System of Record
Marketplace: HubSpot Agent Marketplace, Who publishes agents: HubSpot only: its own pre-built agents, Where the agents work: HubSpot Smart CRM, How agents work together: Not described, Governance it offers: Not described
Marketplace: OpenAI: ChatGPT apps and Frontier, Who publishes agents: Developers, after OpenAI review, Where the agents work: ChatGPT workspaces; Frontier for agents with their own identity, How agents work together: Frontier connects OpenAI-run agents, Governance it offers: Admin allow-lists; identity and permissions in Frontier
For a shorter, general list of what exists, see Is there a marketplace for AI agents?.
What do these marketplaces have in common?
Three patterns run through every row.
Each is anchored to its vendor's data and identity. Salesforce agents work on Salesforce data, Microsoft's on Graph and Entra, Google's on Gemini Enterprise, Oracle's inside Fusion, Workday's on HR and finance records. That is a strength when the work lives in one suite.
Each vendor now sells its own control plane. Microsoft Agent 365, ServiceNow AI Control Tower and Workday's Agent System of Record are each pitched as the place to register and govern agents. Buy from three marketplaces and you can end up with three registries.
Protocols connect messages, not context. A2A and MCP let one agent call another or reach a tool. They do not give agents one shared memory of the customer, the brief and the decisions already made.
A marketplace solves discovery and procurement. It does not solve how the agents you bought will work together.
Why will siloed agents not pass the enterprise bar?
An enterprise does not judge an agent on its demo. It judges it the way it judges a hire: who it reports to, what it may do alone, who checks its work, what it costs and what happens when it is wrong. Siloed agents fail most of those questions.
Nobody owns the outcome. A marketplace agent owns a task: write the email, score the lead, draft the post. Nobody owns the number the tasks are meant to move. You end up as the manager of every agent, routing work between them by hand.
Context stops at the agent's edge. The agent drafting outreach does not know what the research agent found, what the brand team ruled out or what legal approved last week. MIT NANDA's 2025 study of enterprise generative AI put it plainly: "Most GenAI systems do not retain feedback, adapt to context, or improve over time." (source)
Autonomy is a setting, not a record. A siloed agent may do whatever its configuration allows, from the day it is installed. Nothing ties what it may do alone to how well it has done the work.
Checks live in a different tool. Brand rules, claim checks and spend limits sit in separate products, if they exist at all, so the agent that writes is never held by the check that should stop it.
Sprawl outruns governance. Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls. (Gartner) McKinsey's survey The state of AI in 2025 found that 23% of respondents' organisations are scaling an agentic AI system somewhere in the enterprise. The gap between buying agents and running them is an organisation problem, not a catalogue problem.
What does an enterprise-grade agent organisation look like?
You would never hire ten people and give none of them a manager. The same holds for agents. An agent organisation has five layers, each with a clear job.
Layer: AI chief of staff, What it does: Owns a department's number, decides what the week is for, gives each piece of work to its owner and reports back against the target, Who answers to whom: Reports to your people
Layer: Team lead, What it does: Runs one part of the department from its own room, breaks requests down, puts tasks on the board and calls a meeting when one view is not enough, Who answers to whom: Reports to the chief of staff
Layer: Sub-agent, What it does: A specialist seat on a lead's bench that does one kind of job deeply and hands the result back up the line, never sideways, Who answers to whom: Reports to its team lead
Layer: Sentinel, What it does: A check that sits beside the work and can stop it: a claim checker, a brand check, a spend cap or an action that always waits for a person, Who answers to whom: Answers to the rules your people set, not to the agent it checks
Layer: Your people, What it does: Hold the authority: approve what spends money or speaks for the company, and decide every step up an agent takes, Who answers to whom: Accountable for the outcome
Work comes down the line and results go back up it. Nobody has to know which agent does what; the line does.
Why sentinels matter most to an enterprise
A sentinel is what makes a team safe to scale. It is not another worker. It is a check the worker cannot argue its way past. On Nagent, sentinels take four forms:
Checking seats on a team. On HOOK's creative team, the GATE seat checks every claim and line of copy, and can block.
A check in code before anything publishes. Every post passes it, and anything only a writer can fix holds the post, with one sentence per problem.
Budgets. Every agent has daily and monthly caps in credits, checked before every paid call.
Locked actions. Deleting or taking down, redirects, governance changes, lifting a check, spending past a cap, and creating agents, skills, workflows or triggers always wait for a person, whatever the agent's level.
How does Nagent organise agents instead of shelving them?
Nagent is multiplayer AI for growth: people and AI agents in one shared context, delivering the number. We ran an agent marketplace ourselves, and retired it on 9 October 2026, because a list of agents was not what our customers needed. They needed a team.
Departments with a chief of staff. MIRA is the AI Marketing Chief of Staff, SERA the AI Sales Chief of Staff and CREA the AI Content Chief of Staff. N, head of the growth org, sits above them in Beta.
Team leads with benches. DRIS runs organic and AI search visibility, NIA paid marketing, NORA outbound research and HOOK creative strategy (Beta). Each has its own room, and several have sub-agents on their bench.
One shared context. Every agent reads the same Knowledge Hub, the team's memory and the room's record before it acts, so a handoff carries the context with it.
Handoffs with rules. A sub-agent hands work back to its lead or up to the chief of staff, never sideways. Every hop is checked against the receiving agent's budget, and one message travels at most three hops. Asking another department is a written handoff to that room's lead.
Earned autonomy. Every agent holds a level from L1 Observe to L5 Lead and arrives at L2 Assist. Agents are nominated for the next level on their record, and a person decides. On Enterprise, a raise to L5 also needs a second person. Each night the record is read again, and an agent whose work slips drops a level.
It is also open at the edges. You can build your own agent from one sentence in Agent Workbench, extend an agent with knowledge, tools and a level, and connect hundreds of tools through Composio, plus your own API. Developers can run the same agents through a REST API, an MCP server and signed webhooks, governed exactly as inside Nagent (developer docs).
For security reviews: Nagent is ISO/IEC 27001:2022 certified by InterCert, with a SOC 2 Type II programme underway; tenant isolation, RBAC, append-only audit and private VPC are available today.
When is a marketplace still the right choice?
A marketplace is a good answer when the job is narrow and lives in one suite: a service agent inside your ITSM tool, an HR agent inside your HR system, a connector that brings one app into your assistant. Buy it where your data already lives.
It is the wrong answer when the work crosses teams and tools, when someone must own a number, or when the agent will speak for the company. Growth work is all three. There, start from the organisation, and treat individual agents as seats on it.
How should an enterprise evaluate agents, whatever the source?
Ask these seven questions of any marketplace agent or agent platform:
Who owns the outcome? Name the agent or person accountable for the number, not just the task.
Where does context live? Check that agents read one shared record, not each its own copy.
How do agents hand off? Look for written handoffs with a route, a budget check and a record.
What may it do alone, and who decided? Autonomy should be earned on a record and set by a person.
Which checks can stop it? Claim checks, brand checks, spend caps and actions that always wait.
How many control planes will you run? Count the registries you would need across vendors.
What does it cost before it runs? Spend should be visible and capped per agent.
Frequently Asked Questions
Is there an AI agent marketplace for enterprise?
Yes. Salesforce AgentExchange, Microsoft's Agent Store, Google Cloud Marketplace for Gemini Enterprise, AWS Marketplace's AI Agents and Tools, the ServiceNow Store, Oracle's Fusion AI Agent Marketplace and Workday's Agent Partner Network all sell agents to enterprises, mostly on the account and data platform you already use.
What is the best AI agent marketplace for an enterprise?
The one attached to the platform where the work already lives, for a narrow task. For work that crosses teams and tools, such as growth, compare agent organisations rather than marketplaces: who owns the outcome, how context is shared and which checks can stop an agent.
Why do siloed AI agents fail in enterprises?
They own a task but not an outcome, they do not share context with other agents, their autonomy is a setting rather than a record, and their checks live in other tools. The result is sprawl that a person has to manage by hand.
What is an AI chief of staff?
An AI agent that runs a department and owns its number. It decides what the week is for, gives each piece of work to the right team lead and reports back against the target. On Nagent, MIRA, SERA and CREA are the AI chiefs of staff for marketing, sales and content.
What is a sentinel in an agent team?
A check that sits beside the work and can stop it, and answers to the rules your people set rather than to the agent it checks. Examples are a claim-checking seat, a check before anything publishes, a spend cap and actions that always wait for a person.
What should you do next?
If your agent plan is a list of agents, add the org chart first. Read how a Nagent team is built, see the autonomy and governance rules, or book a demo and watch a chief of staff, its team leads and their sentinels take one request to approved work.