IBM's AgentOps Tools Ensure AI Agents Perform as Expected
IBM's AgentOps, unveiled at Think 2025, provides tools for developers to monitor and optimize AI agents, ensuring they operate as intended across various workflows.
Generative AI has rapidly evolved from proof-of-concept demos to a transformative force across industries, with potential economic benefits reaching $4.4 trillion. At the heart of this revolution are AI agents—autonomous systems capable of tasks like answering queries, monitoring sensors, and coding projects without step-by-step guidance. IBM recently showcased a suite of agents at Think 2025 designed to streamline repetitive tasks in HR, procurement, and sales.
The Challenge of AI Agent Reliability
Despite their potential, AI agents introduce new complexities. Their dynamic workflows, non-deterministic logic, and interactions with APIs, tools, and other agents make traditional monitoring and debugging methods inadequate. Developers and users need assurance that these agents will perform consistently and accurately.
Introducing AgentOps
IBM Research has developed AgentOps, a set of tools to provide visibility into AI agent operations. AgentOps enables developers to:
- Track decision-making processes
- Monitor memory states
- Analyze tool usage
- Detect anomalies and regressions
- Compare real-time performance against historical data
The goal is not just observability but continuous improvement and accountability.
Key Features of AgentOps
- OpenTelemetry Integration: AgentOps builds on OpenTelemetry (OTEL) standards, supporting frameworks like LangChain, watsonx, CrewAI, and LangGraph. It treats agents, tasks, and tools as core system components, ensuring seamless data flow.
- Open Analytics Platform: This platform offers detailed insights into agent behavior, with AI-powered analytics that provide multi-trace workflow views and trajectory explorations. It can also recommend optimizations for accuracy, latency, and cost.
- Extensibility: Researchers and practitioners can easily add new metrics or analytical methods.
Real-World Applications
IBM has already used AgentOps to develop agents for products like Instana, Concert, and Apptio. As AI agents become more human-like in their ability to adapt mid-task, AgentOps ensures they remain transparent and controllable within familiar developer environments.
The Future of Agentic AI
With AgentOps, IBM aims to make AI agents more reliable, efficient, and cost-effective, empowering organizations to harness their full potential. The tools will be integrated into IBM’s watsonx and Instana platforms, bringing observability to increasingly autonomous systems.
"AgentOps isn’t just about monitoring—it’s about making AI agents iteratively improve and adapt," says IBM’s research team. The technology promises to bridge the gap between innovation and trust in the AI-driven future.
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About the Author

Dr. Emily Wang
AI Product Strategy Expert
Former Google AI Product Manager with 10 years of experience in AI product development and strategy formulation. Led multiple successful AI products from 0 to 1 development process, now provides product strategy consulting for AI startups while writing AI product analysis articles for various tech media outlets.