IBM and Microsoft's Imagine Enterprise starts with more than 6,000 pre-built AI agents and assets

More than 6,000 prebuilt AI agents and assets. That is the opening inventory IBM Consulting brings to Imagine Enterprise, the agent platform from IBM and Microsoft.

Both companies posted the joint announcement to their newsrooms on Nov. 10, 2026. It describes a platform that gives enterprises a way to build, govern and run AI agents, with those agents supporting people as they carry out processes specific to their industry. Regulated industries are a particular focus. Microsoft provides the AI, data, cloud and security foundation. For qualifying engagements, both companies assign forward-deployed engineers to help customers get from experimentation into production.

For a CIO at a bank or an asset manager, the interesting question is what exactly is being bought. A software platform? A library of starting points? Or a delivery engagement with two vendors' engineers in the room? The structure of the offer points toward the third, and that changes how a pilot should be scoped and judged.

Who builds which layer

The division of labor is unusually explicit. IBM Consulting's agents and assets are built on IBM Consulting Advantage, which IBM describes as its AI-powered delivery platform. Three of its components, Process Studio, Context Studio and Advantage Core, are the tools the companies say help organizations design, deploy and govern tailored agents. Microsoft's side covers coordination and the foundation underneath.

• Coordinating agents and enterprise systems: Microsoft Foundry, GitHub Copilot, Microsoft Copilot Studio
• Data, identity, AI governance, security and cloud: Microsoft Fabric, Microsoft 365 infrastructure, Microsoft Entra, Microsoft Purview, Microsoft Defender, Microsoft Azure
• Designing, deploying and governing tailored agents: IBM Consulting Advantage's Process Studio, Context Studio and Advantage Core

The governance story is built from products with names a security team already knows. That is the practical appeal for regulated firms: the release says the platform governs agents with controls, observability and AI-compliance support, and those functions rest on Microsoft's identity and data-protection products. For organizations already running Entra for identity and Purview for data governance, agent oversight could plug into tooling their compliance staff already understand, rather than arriving as a new console to certify.

The coordination row matters more than it looks. A March 2025 arXiv survey of AI agents by Naveen Krishnan describes hierarchical agents, in which higher-level agents break complex tasks into smaller ones and hand them to lower-level agents that report progress back. It lists enterprise automation platforms that coordinate across specialized subsystems among its examples. If a firm strings several starter agents into one industry process, that handoff is the work the Foundry and Copilot Studio layer would carry.

The release counts more than 6,000 AI agents and assets together without a split, so the number describes the size of IBM Consulting's starting library, not a count of deployable agents. The release also states no price.

Engineers ride along on qualifying deals

The companies pitch Imagine Enterprise as the alternative to starting from scratch, replacing platforms a firm already depends on, or spending months assembling the pieces itself. They frame it around three design claims. Organizations start with capabilities already built for their industry and adapt them. The platform connects to existing data, processes, systems and technology, so a firm can start where it is and scale over time. And it puts IBM Consulting's experience building and running those 6,000-plus agents together with Microsoft tools that connect enterprise knowledge and context across people and agents.

Imagine Enterprise packs three things into one offer: a starter library of more than 6,000 agents and assets, Microsoft's identity, security and governance tools, and engineers from both companies to connect them to systems a firm already runs.

That packaging suggests procurement will feel more like scoping a consulting engagement than licensing a tool. The engineering support is tied to "qualifying engagements," and the agents come out of IBM's delivery platform. The strongest counterpoint comes from the release itself: the platform is built to sit on existing environments, so for a firm already deep in Azure and Microsoft 365, the incremental commitment may be smaller than the size of the bundle implies.

Two of those three routes run through IBM Consulting, which fits the engagement-shaped reading. Self-management is the route for a firm whose own platform team wants to run the agents. Because the forward-deployed engineering is tied to qualifying engagements, the route a buyer picks could shape how much of that hands-on help shows up in practice.

The industry claim also starts within set limits. At launch, Imagine Enterprise's industry-specific frameworks cover financial services, insurance and retail. The companies call these frameworks a repeatable blueprint that could later extend to other industries, such as manufacturing, energy, public sector and mobility. The launch customer is a financial services firm, which puts it squarely inside the first wave.

SEI, still in the exploring phase

SEI, which provides financial technology, operations and asset management services, is exploring emerging and agentic technologies with IBM Consulting, with Microsoft's support, and is using other IBM Consulting and Microsoft technologies to improve operations and the experience it delivers to employees and clients. The company manages, advises or administers approximately $2 trillion in assets as of Sept. 30, 2026, according to its boilerplate in the release. Sneha Shah, SEI's chief AI strategist and head of SEI Next, described the approach in the announcement.

Shah added that the work lets SEI explore whether new capabilities might support critical processes and then grow responsibly over time. The order is telling. Critical processes come first, and scale is something earned later.

Exploring is the honest word for where SEI sits, and it matches the problem the release says the engineers are there to solve: the gap between experimentation and production.

The vendors' executives set a bigger bar. Mohamad Ali, senior vice president and head of IBM Consulting, said clients have stopped looking for AI that only lets people do their existing work slightly faster. In his view, what they now want is an agentic business, one that brings human expertise and digital labor together at enterprise scale. Rodrigo Kede Lima, president of Microsoft Frontier Company, said the organizations that win at enterprise AI will be the ones able to put their data, their industry expertise and AI to work in action.

Both framings point past productivity toward outcomes. Outcomes, helpfully, are something a buyer can count.

Finished tasks as the yardstick

If the pitch is faster deployment, the buyer needs a measure of what fast means for agents. A useful one comes from Arm. Jeff Defilippi, Arm's senior director of product management, argued that a single agent task can chain model calls, retrieval, database transactions, code execution, tool calls and state changes, with each step shaping the next. In that setting, he said, successful task completion is the meaningful unit of performance, not any single component metric.

Defilippi told The Next Platform that many agentic workflows have "both a latency problem and a concurrency problem." An agent may invoke a tool, wait for a database or API response, update its state and only then decide what to do next, so delay accumulates along that chain even when other parts of the system run in parallel. At low concurrency, he said, waiting on a dependency has a large effect on an individual task's completion time; as more agents run at once, throughput and scheduling efficiency take over.

That maps neatly onto the promise to fit existing environments. An agent wired into a firm's current systems would spend part of every task waiting on them, which suggests a pilot's completion clock measures the surrounding stack as much as the agent. Arm's advice to test under realistic load and concurrency, rather than relying on lightly loaded peak results, applies here too: one agent running in a quiet sandbox would flatter the number.

The Arm feature is sponsored, and Arm has a stake in how CPUs get judged. Its measurement logic still travels well beyond chip shopping.

Krishnan's survey names reconciling financial statements among the complex tasks AI agents can take on. Work shaped like that is judged by whether the statement gets reconciled, correctly and on time, which is exactly the end-to-end view Arm is pushing.

The survey also cites Kapoor et al. (2024) to argue that agent benchmarks often focus narrowly on accuracy without enough attention to cost-effectiveness, reproducibility and real-world applicability. For a reconciliation agent, that suggests a scorecard with more than one line: whether the statements reconcile, what each completed run costs, and whether the result holds when the run is repeated.

That framing gives a buyer bargaining power. A starter library shortens the build only if the adapted agents complete real work inside the firm's own data and controls, and a completion-time baseline taken before engineers arrive is the cleanest way to show whether they did.

The signal that settles it

A public production deployment on Imagine Enterprise that names the process and shows before-and-after completion times could turn the count of more than 6,000 agents and assets from an inventory into evidence. Whether SEI or another regulated firm is the one to publish it is the milestone worth watching.