IBM Unveils Project Bob and Expands AI Infrastructure to Boost Enterprise Productivity

IBM claims 45% productivity gains with Project Bob, its multi-model IDE that orchestrates LLMs with full repository context

For many enterprises, there continue to be barriers to fully adopting and benefiting from agentic AI. IBM is betting the blocker isn't building AI agents but governing them in production. At its TechXchange 2025 conference today, IBM unveiled a series of capabilities designed to bridge the gap: Project Bob, an AI-first IDE that orchestrates multiple LLMs to automate application modernization; AgentOps for real-time agent governance; and the first integration of open-source Langflow into watsonx Orchestrate, IBM's platform for deploying and managing AI agents. IBM's announcements represent a three-pronged strategy to address interconnected enterprise AI challenges: modernizing legacy code, governing AI agents in production and bridging the prototype-to-production gap.. The company claims 6,000 internal developers within IBM have used Project Bob, achieving an average productivity gain of 45% and a 22-43% increase in code commits . Project Bob isn't another vibe coder, it's an enterprise modernization tool There is no shortage of AI-powered coding tools in the market today, including tools like GitHub Copilot and vibe coding tools such as Replit, Cursor, Bolt and Lovable. "Project Bob takes a fundamentally different approach from tools like GitHub Copilot or Cursor," Bruno Aziza, IBM's Vice President of Data, AI and Analytics Strategy told VentureBeat. Aziza said that Project Bob is enterprise-focused and maintains full-repository context across editing sessions. It automates complex tasks like Java 8 to more modern version of Java and framework upgrades from Struts or JSF to React, Angular or Liberty. The architecture orchestrates between Anthropic's Claude, Mistral, Meta's Llama and IBM's recently released Granite 4 models through a data-driven model selection approach. The system routes tasks to whichever LLM is best suited, balancing accuracy, latency and cost in real time. "It understands the entire repository, development intent and security standards, enabling developers to design, debug, refactor and modernize code without breaking flow," he said. Among 6,000 early adopters within IBM, 95% used Bob for task completion rather than code generation. The tool integrates DevSecOps practices like vulnerability detection and compliance checks directly into the IDE. "Bob goes beyond code assistance -- it orchestrates intelligence across the entire software development lifecycle, helping teams ship secure, modern software faster," he said. Project Bob benefits from new Anthropic partnership Part of Project Bob is a new partnership between IBM and Anthropic The two vendors announced a partnership to integrate Claude models directly into the watsonx portfolio, starting with Project Bob. The collaboration extends beyond model integration to include what IBM describes as a first-of-its-kind guide for enterprise AI agent deployment. IBM and Anthropic co-created "A Guide to Architecting Secure Enterprise AI Agents with MCP Servers," focused on the Agent Development Lifecycle (ADLC). The ADLC framework provides a structured approach to designing, deploying and managing enterprise AI systems. MCP refers to Model Context Protocol, Anthropic's widely embraced open standard for connecting AI assistants to the systems and data they need to work with. Making it easier to build enterprise-grade AI agents In addition to Project Bob, IBM announced that it is extending its watsonx Orchestrate technology to integrate the open source Langflow visual agent builder. Langflow is an open-source technology that is led by DataStax, which itself was acquired by IBM in May of this year. The integration of Langflow is intended to address what Aziza calls the "prototype to production chasm." "Today, there's no seamless path from open-source prototyping to enterprise-grade systems that are reliable, compliant and scalable," Aziza said. "Watsonx Orchestrate transforms Langflow-like agentic composition into an enterprise-grade orchestration platform by adding governance, security, scalability, compliance, and operational robustness -- making it production-ready for mission-critical use." Aziza explained that the integration of Langflow with watsonx Orchestration brings critical capabilities on top of the open-source tool including: Agent lifecycle framework: Provisioning, versioning, deployment and monitoring with multi-agent coordination and role-based access. Integrated AI governance: Embedded watsonx.governance provides audit trails, explainability for agent decisions, bias and drift monitoring and policy enforcement. Langflow has no native governance controls. Enterprise infrastructure: SaaS or on-premises hosting with data isolation, SSO/LDAP integration and fine-grained permissions. Langflow users must manage their own infrastructure and security. No-code and pro-code options: Langflow is "low-code." IBM added a visual, no-code Agent Builder and a pro-code Agent Development Kit for seamless promotion from prototype to production. Pre-built domain agents: Catalog of HR, IT and finance agents integrated with Workday, SAP and ServiceNow. Production observability: Built-in dashboards, analytics and enterprise support SLAs with continuous performance monitoring. AgentOps and Agentic Workflows: From building to governing IBM is also introducing two new capabilities to watsonx Orchestrate that work in tandem with the Langflow integration: Agentic Workflows for standardized agent coordination and AgentOps for production governance. Agentic Workflows addresses what Aziza calls the "brittle scripts" problem. Today developers build agents using custom scripts that break when scaled across enterprise environments. Agentic Workflows provides standardized, reusable flows that sequence multiple agents and tools consistently. This connects directly to the Langflow integration. While Langflow provides the visual interface for building individual agents, Agentic Workflows handles the orchestration layer, coordinating multiple agents and tools into repeatable enterprise processes. AgentOps then provides the governance and observability for those running workflows. The new built-in observability layer provides real-time monitoring and policy-based controls across the full agent lifecycle. The governance gap becomes concrete in enterprise scenarios. Without AgentOps, an HR onboarding agent might set up benefits and payroll but teams lack visibility into whether it's applying policies correctly until problems surface. With AgentOps, every action is monitored in real time, allowing anomalies to be flagged and corrected immediately. What this means for enterprises Technical debt is something that many organizations struggle with and it can represent a non-trivial barrier for organizations looking to get into agentic AI deployments. Project Bob's value proposition is clearest for organizations with significant legacy Java codebases. The 45% productivity gains IBM measured internally suggest meaningful acceleration for Java 8 to more modern versions of Java and framework upgrades from Struts or JSF to modern architectures. However, these metrics come from IBM developers working on IBM systems. The critical unknown is whether the multi-model orchestration delivers the same results on customer codebases with different architectural patterns, technical debt profiles and team skill levels. The Langflow integration addresses a genuine gap for teams already using open source agent frameworks. The challenge isn't building agents with tools like LangChain, LangGraph or n8n. It's adding the governance layer, lifecycle management, enterprise security controls and observability required for production deployment. For enterprises looking to lead in AI adoption, IBM's announcements serve to reinforce the fact that governance infrastructure is now table stakes. You can build agents quickly with existing tools. Scaling them safely requires the lifecycle management, observability and policy controls. Project Bob is now available in private tech preview with broader availability expected in the future. IBM is accepting access requests through its developer portal. Its AgentOps and agentic workflows integrations are now available in watsonx Orchestrate, while its Langflow integration is expected to be generally available at the end of this month.

IBM expands agentic AI and infrastructure automation to bridge software, cloud and mainframe systems - SiliconANGLE

IBM expands agentic AI and infrastructure automation to bridge software, cloud and mainframe systems IBM Corp. is using its annual TechXchange conference in Orlando this week to announce a slate of software and infrastructure updates aimed at helping enterprises put artificial intelligence into operation across hybrid environments. Spanning agentic AI orchestration to mainframe automation and infrastructure intelligence, they underscore IBM's intention to embed AI across its software and hardware lines. The new offerings are designed to help customers transition from experimentation to scalable deployment, said Bruno Aziza, IBM's vice president of data, AI, and analytics strategy. "Customers are struggling to get value from their investment," he said. More than 300 customer sessions at the event exhibit deployments of agentic systems in production. Aziza said IBM's vision differs from competitors by emphasizing interoperability and hybrid operations. "What makes our approach different is that while everyone is providing a siloed, vertically integrated solution for building agents, we think about how all these agents that have been built across multiple platforms are going to be orchestrated and operated at scale," he said. Headlining the slate of IBM's AI announcements are new features for watsonx Orchestrate, a platform encompassing more than 500 tools and customizable, domain-specific agents from IBM and its partners. Designed to be tool-agnostic and adaptable to any environment, Orchestrate is meant to enable scalable deployment and governance of AI agents. New capabilities include AgentOps, a governance and observability layer that provides lifecycle monitoring and policy-based control for AI agents in production. IBM said the tool makes agent behavior more transparent and secure through real-time monitoring. Aziza said AgentOps extends IBM's orchestration framework by enabling customers to consistently test and govern agents. "You can get real-time monitoring and policy-based controls so you can not only build and orchestrate these agents, but also run them in production," he said. To simplify development, IBM is introducing Agentic Workflows, which enable teams to reuse and scale multi-agent processes, as well as integraton with Langflow, a drag-and-drop visual application builder. The company said the enhancements help both developers and business users build and deploy agents quickly. Six months after announcing its latest generation of mainframes, the watsonx Assistant is bringing agentic capabilities to the Z platform. The goal is to make AI accessible across all mainframe users, from system administrators to developers, said Tina Tarquinio, chief product officer for IBM Z and LinuxONE, the Linux-based version of Z. The new version of Watsonx Assistant for Z moves "from question and answer to question and action," she said, enabling agents to perform multi-step workflows, maintain conversational context, and make informed decisions. The platform features an agent builder with a low-code interface, enabling users to design custom agents tailored to specific enterprise processes. "It can evaluate tradeoffs and understand the pros and cons of any action," Tarquinio said, adding that the framework delivers the security, compliance and scalability features that are central to regulated industries. IBM also announced the general availability of the IBM Spire Accelerator, a purpose-built AI processor for mainframe and LinuxONE systems that debuted in April. Designed as a collaborative effort between IBM Research and IBM Infrastructure, Spire supports generative and agentic AI workloads with low latency and power consumption. An IBM Z17 can house up to 48 Spire cards, each equipped with 32 cores and drawing about 75 watts of power. In the first major product announcement to emerge from IBM's $6.4 billion acquisition of HashiCorp in February, IBM introduced Project infragraph, a new capability within the HashiCorp Cloud Platform that provides a real-time knowledge graph for enterprise infrastructure observability. It's "a unified knowledge graph for infrastructure, connecting application workloads with their related components and preparing enterprises for their AI-driven automation," said Kyle Ruddy, senior director of product marketing at HashiCorp. Ruddy said the offering is aimed at organizations struggling with fragmented tooling and reactive operations. "Cloud estates are larger and more distributed than ever, making it impossible for teams to maintain visibility with traditional tools," he said. "Project infragraph collapses that timeline to minutes," he said. The new platform ingests data from cloud providers, Kubernetes clusters, and self-hosted environments to create a live relational view of dependencies across services. It integrates with ServiceNow's digital workflow platform and security tools from Palo Alto Networks Inc. and Wiz Inc., Future plans are to connect to IBM's Red Hat Ansible, OpenShift, watsonx Orchestrate, Concert, Turbonomic and Cloudability products. "Project infragraph is more than just a new capability; it's a foundation for managing infrastructure in the AI era," Ruddy said. The product is currently in beta test and no availability date has been announced. Also new is Guardian Cryptography Manager, a platform designed to help enterprises manage cryptographic keys, certificates and algorithms across complex hybrid environments. The product addresses the growing challenge of cryptographic sprawl and the coming shift to quantum-safe algorithms. "Only 45% of organizations say that they have full visibility into their cryptographic estate," said Vishal Kamat, vice president of data security at IBM. "Most companies today manage certificates on a spreadsheet, and that's just not scalable." The new platform provides automated discovery, risk assessment and lifecycle management of cryptographic assets to support "crypto agility," or the ability to update encryption methods without disrupting operations. Kamat noted that regulations are tightening. The frequency with which companies will need to update their certificates is expected to drop from the current 398 days to just 47 days by 2029. Quantum computers are also expected to render most current cryptographic algorithms obsolete. Enterprises need automation to keep up, Kamat said. For software developers, IBM is previewing Project Bob an AI-based tool system with task generation capabilities for enterprise software development lifecycles. IBM said Project Bob goes beyond AI coding assistants to work alongside developers to write, test, upgrade and secure software. It works with the Claude, Mistral, Llama and IBM Granite large language models. Features include support for the Java, JavaScript/TypeScript, Python, RPG, Go, C#, Rust, PHP and Kotlin programming languages; automated upgrades to Java code, framework migrations and multistep refactoring across large codebases. Project Bob can coordinate coding, testing and remediation tasks while preserving context across sessions. Security features include "shift-left" vulnerability scanning, accelerated FedRAMP hardening and migration to quantum-safe cryptography. Finally, IBM and Anthropic PBC said they have formed a strategic partnership to advance enterprise-ready AI by integrating Anthropic's Claude LLMs into IBM's software portfolio. The first integration is within IBM's new AI-first integrated environment for enterprise software development and modernization. Currently in private preview, the IDE is being used by more than 6,000 IBM early adopters, who report average productivity gains of 45% without compromising code quality or security, IBM said. The partnership reflects growing enterprise demand for AI solutions that can move seamlessly from experimentation to production. By combining Claude's generative AI capabilities with IBM's strengths in hybrid cloud, enterprise software delivery and regulated industry compliance, the companies hope to accelerate secure AI deployment in large-scale IT environments.