IBM’s agentic AI strategy at Think 2025

At IBM Think 2025, IBM doubled down on a bold agentic AI strategy to unify digital labor across hybrid enterprises. With the rise of AI agents that can reason, collaborate and operate autonomously, businesses are racing to transform siloed automation efforts into orchestrated, enterprise-wide digital workforces, according to Scott Hebner, principal analyst at theCUBE Research and host of “The Next Frontiers of AI” podcast. IBM’s vision aims to bring interoperability, flexibility and trust to this new world by enabling any agent, on any platform, to work across any workflow.

Agentic AI is quickly emerging as a pivotal evolution in enterprise technology. It’s reshaping how companies manage processes, empower employees and create value. IBM’s approach stands out by focusing on open standards, semantic control planes and an architecture that invites a diverse ecosystem of agents.

“We’re seeing a critical inflection point,” Hebner said. “IBM is applying the same interoperability playbook that made it a leader in e-business and hybrid cloud.”

Why openness matters in the age of agentic AI

The scale and complexity of enterprise ecosystems demand AI agents that are not just intelligent, but also interoperable. IBM’s announcements at Think 2025 centered around this very idea, championing openness as the foundation of digital labor. With watsonx Orchestrate positioned as the orchestrator of the agentic enterprise, IBM introduced a platform capable of managing, coordinating, and integrating agents built with any framework.

“Over the next three years, one billion agents will be built on the basis of generative AI,” Rob Thomas, senior vice president, software and chief commercial officer at IBM, said during IBM Think. “They will need to work with each other seamlessly.”

The semantic control plane at the heart of watsonx Orchestrate enables AI agents to interpret goals, decompose them into executable tasks and route those tasks to the right digital workers. This allows agents to operate collaboratively across hybrid environments —on-prem, cloud and software-as-a-service — without being trapped in vendor-specific ecosystems.

“We help our clients integrate,” Arvind Krishna, chief executive officer of IBM, said during IBM Think. “We want to meet them where they are.”

With multi-agent collaboration, pre-built domain-specific agents, and connectors for over 80 enterprise apps, IBM is building a semantic operating system for AI agents, enabling businesses to plug and play digital labor as easily as integrating software components.

IBM’s long game: From middleware to multi-agent orchestration

This isn’t IBM’s first time betting on openness. The company famously rode the e-business wave with IBM WebSphere and captured cloud relevance with Red Hat OpenShift. Now, IBM is applying that same strategy to the agentic AI space, hoping to become the de facto backbone of multi-agent collaboration across enterprise landscapes, according to Hebner.

“It’s IBM deja vu, in so many ways,” Hebner said. “But the stakes are even higher.”

Through its new Agent Connect partner program, IBM is opening the doors for SaaS vendors, integrators, and developers to contribute agents to the watsonx ecosystem, according to Hebner. These agents can be built using any stack and plugged directly into IBM’s orchestration framework, where they benefit from observability, governance and semantic interoperability.

“Rather than forcing a top-down agent architecture, IBM is enabling composability,” Hebner said. “That’s a future-proof play.”

This approach also aligns with emerging enterprise realities: Labor accounts for over 60% of operational costs, while software spending hovers near five percent, according to Hebner. By directly targeting digital labor, IBM is opening the door to value creation beyond traditional IT.

A strategic foundation, not the final destination

Despite its strengths in interoperability and orchestration, IBM still has work to do in the race for decision intelligence, according to Hebner. Platforms from competitors such as Microsoft Corp. and SAS Institute are actively integrating causal AI, advanced reasoning and knowledge graph capabilities to help AI agents make better decisions, not just automate workflows.

Still, IBM’s foundational layer provides a strong starting point. Its modular architecture, emphasis on open large language models and multi-agent orchestration tools make it easier for enterprises to experiment, scale and govern AI use across functions, according to Hebner.

“IBM isn’t trying to own the agentic stack,” Hebner said. “They’re building the connective tissue.”

As AI agents become the norm rather than the exception, IBM’s strategy may prove critical in preventing fragmentation and technical debt, Hebner noted. Whether it will repeat past success depends on how well it balances openness with innovation in reasoning, learning and domain expertise.

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