The measured leap: AI agent observability
Appraising multiagent systems impact with agent operations
Explore approaches, metrics, and AI agent observability solutions to govern, monitor, and enhance performance against business and operational goals. These recommendations can help you progress faster on the path to agent-enabled workforce transformation—while protecting against unexpected costs and business risks.
Key takeaways about AI agent observability, governance, and operations
- AI agent observability is a technology-enabled, people-powered capability that helps organizations see, understand, and optimize the performance of AI agents against desired objectives.
- AI agents can enable enterprises to shift human roles from execution to oversight, opening opportunities for improved productivity.
- New KPI frameworks and solutions will be needed to help appraise the performance and impact of AI agents while safeguarding against new and emergent risks.
- Business process decomposition—whereby existing processes are broken down into discreet tasks—can help identify not only where agents may provide value, but also the metrics and solutions needed to monitor, analyze, and optimize their performance.
- An adaptable AI agent operations capability can help streamline AI agent observability and implementation across a range of agentic AI use cases.
From human in the loop to human on the loop
Most organizations use large language models and prebuilt agents to help humans execute tasks. By shifting human responsibilities toward supervision and training, multiagent systems make new realms of productivity, speed, scalability, and efficiency possible.
Defining metrics of success and supporting agent operations
The shift to “human on the loop” is a form of automation that requires strong AI agent governance and oversight. Multiagent systems can be continuously trained to improve, but without proper transparency and monitoring, they can also go haywire.
In essence, agent operations serves as the performance and risk management function for digital workers and teams, providing the enterprise with alerts and insights about their activity and impact. The complex ways that AI agents work to drive impact demand a comprehensive KPI framework for assessing performance, previewed below. Download the report for more detail about KPIs, business process decomposition, and how it all comes together in a reference architecture for implementing agent operations.
|
Cost |
Speed |
Productivity |
Quality |
Trust |
|---|---|---|---|---|
|
Purpose: Monitor and optimize the cost of operating agentic systems over time |
Purpose: Monitor and identify potential opportunities to improve latency of systems and components |
Purpose: Monitor and identify potential opportunities to improve system throughput |
Purpose: Monitor and identify potential opportunities to improve system response quality |
Purpose: Monitor and measure human user feedback trends |
|
Example KPIs: Cost, token usage |
Example KPIs: Retrieval latency, generation latency, tool call latency |
Example KPIs: Success rate, productivity gain, average handling time |
Example KPIs: Tool selection efficiency, correct tool utilization, plan efficiency |
Example KPIs: User feedback scores, usage metrics |
Explore more from our AI agents series
Dive into our ongoing series to build your understanding of autonomous AI and get tangible recommendations on how to move forward.
Learn about how AI agents can help you rewrite the rules of automation to unlock efficiency and value.
Get a deeper look at multiagent systems are transforming organizations and industries.
Understand design principles and reference architectures underlying multiagent systems.
Discover the path to agentification and the cost, workforce, and risk factors you’ll face along the way.
Learn earn how to build an agentic enterprise through autonomous AI and find out what to expect in the near term.
Opens in new window
Improve business outcomes with AI agent observability
The transition from human in the loop to human-led, agent-enabled operations demands a thoughtful approach to agent architecture and process design, performance measurement, and continuous oversight. Effective design and deployment of digital workers is not simply about automating tasks. It’s about reimagining how work gets done, how value is created… and how digital worker performance is made observable and actionable.
Prakul Sharma
Parth Patwari
Deloitte's AI and Data services
Unlock the power of data with AI to generate business insights and evergreen value.
Contributors to this report: Jim Rowan, Danielle Boutwell, Giannis Doulamis, Pradeep Gorai, Siva Muthu, SanghamitraPati, Laura Shact, Greg Vert