AI agents are transforming industries by automating complex workflows and tackling tasks that once required human expertise. With generative AI, these agents are more autonomous than ever, independently gathering information, coding, and adapting to new challenges. However, this autonomy introduces unpredictability and risk, making it difficult to ensure agents behave reliably and safely at scale.
Why Observability Matters
Traditional monitoring tools fall short when it comes to today’s dynamic, non-deterministic AI agents. Developers need to see not just what agents are doing, but how they make decisions, recall information, and interact with their environment. Without deep visibility, debugging becomes guesswork and scaling up means accepting unknown risks.
AgentOps: IBM’s Toolkit for AI Agent Transparency
To address these gaps, IBM Research has launched AgentOps, a toolkit designed to give developers detailed oversight of agentic systems. Showcased at IBM Think 2025, AgentOps is built for real-world workflows and offers essential features, including:
- Decision and memory tracking for a comprehensive view of agent reasoning over time
- Anomaly and regression detection to catch issues as they arise
- Behavior comparison with historical data for ongoing improvement
- Automated recommendations to optimize performance and accountability
Open Standards and Advanced Analytics
AgentOps stands out for its foundation on OpenTelemetry (OTEL), the leading open-source observability standard. This ensures compatibility with popular agent frameworks like LangChain, watsonx, CrewAI, and LangGraph. Agents, tasks, and tools are all treated as core system components, enabling smooth data sharing across the entire stack.
IBM watsonx HR agents are available today, and can help automate workflows for employee support, such as time off management, profile updates, leave and benefits–integrating with popular HR systems and human capital management applications.
IBM watsonx Procurement agents are designed to streamline procurement workflows such as procure to pay, supplier assessment, and vendor management processes, integrating with tools like Sirion and Dun & Bradstreet.
IBM watsonx Sales agents built to automate sales processes, help identify new prospects, support outreach to qualified leads, and optimize research and enablement–connecting to technologies from Salesforce, Seismic, and Dun & Bradstreet.
Beyond observability, IBM has layered in a robust analytics platform. Users can dig into agent behaviors, create custom metrics, and leverage AI-powered insights to refine workflows. These analytics help teams pinpoint inefficiencies, optimize costs, and enhance accuracy—all critical for deploying agents at enterprise scale.
Enterprise Integration and Extensibility
AgentOps is already in use with IBM’s leading automation tools, such as Instana, Concert, and Apptio, and integrates seamlessly with watsonx Orchestrate. This enterprise-ready approach ensures organizations can monitor, troubleshoot, and improve their agentic AI solutions with confidence.
The platform’s extensibility means it evolves alongside agentic technology. As agents become more capable of self-correction and adaptation, observability tools like AgentOps provide the real-time feedback loops both humans and machines need to drive continuous improvement.
Building Trust in Agentic AI
IBM’s AgentOps marks a major leap in making AI agents transparent, reliable, and continuously improvable. By combining open standards, detailed analytics, and enterprise integration, AgentOps empowers organizations to embrace agentic AI—knowing they have the tools necessary for visibility, accountability, and performance optimization at every stage.
Source: IBM Research Blog, "How to know if your AI agents are working as intended," by Mike Murphy, June 24, 2025.
IBM’s AgentOps Brings Observability to AI Agents