IBM's AgentOps Tools Ensure AI Agents Perform as Expected

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

1. 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.
2. 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.
3. 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.