How to Improve AI Agent Performance with Real-Time Evaluation Metrics

The Challenge of AI Agent Hype

The hype around AI agents has reached new heights, but this doesn't necessarily translate to successful projects. A recent McKinsey 2024 generative AI study found that inaccuracy is now the top concern for enterprise leaders. As one AI expert noted, "What happens when you attach 100 agents together, and each of them is 98 percent accurate? That's a pretty big compound error problem."

Ten Steps for Better AI Agent Results

1. Match AI accuracy to viable use cases – Ensure the level of accuracy achievable aligns with the use case requirements.
2. Maximize LLM accuracy – Use methods like RAG, LLMs-as-auditors, and task-specific agents.
3. Design for human supervision – Incorporate human oversight based on compliance and customer needs.
4. Leverage deterministic automation – Combine probabilistic (LLM) and deterministic (RPA) systems for better results.
5. Hold AI projects accountable – Use standard business metrics to measure ROI.
6. Address liability and IP issues – Guard against biases and legal exposure in data sets.
7. Engage stakeholders – Involve users in the AI narrative to build trust and reduce job loss fears.
8. Establish governance frameworks – Manage AI agents and their interactions across vendors.
9. Use evaluation tools – Measure and improve agent accuracy, RAG, and prompt engineering.
10. Avoid waiting for the next big model – Today's models are sufficient for many use cases.

The Role of AI Evaluation Tools

Galileo, a leader in AI evaluation, breaks down agent performance into three key metrics:

• Tool Selection Quality (TSQ) – Measures whether the right tool was chosen for the task.
• Tool Error Rate – Tracks errors in tool execution.
• Task Completion/Success – Evaluates whether the agent completed its goal.

(Galileo flags a RAG "context adherence" problem – LLM hallucination)

Galileo's approach includes real-time corrections and continuous learning with human feedback (CHLF). For example, their RAG "completeness" score can be 100%, but if the LLM ignores the context, the "context adherence" score drops to 0, leading to hallucinations.

Burning Questions Answered

1. Compound Error Problem – Galileo CTO Atin Sanyal emphasizes guardrails like completeness metrics to mitigate compounding errors. "Putting in metric checks helps detect quality hotspots," he says.
2. Early Wins in Agent Evaluation – Sanyal suggests focusing on "macro" metrics like tool selection quality and "micro" metrics like reroute counts in tool overlaps.

(Galileo core AI agent metrics screen shot)

The Mechanics of AI Trust

AI evaluation is more than just a vendor tool—it's a mindset. Companies must adopt protocols like the Model Context Protocol (MCP) to ensure interoperability across agents. As AI evolves, approaches like Active Inference and the LLM-Modulo Framework offer alternatives to traditional LLM limitations.

"AI is only as good as the data" is partly true, but even the best data can't solve all pitfalls. Evaluation intelligence builds trust through visibility and feedback, closing the gap between hype and reality.