Evaluating AI Agents: From Hype to Business Value with the Higher Framework

AI agent adoption often outpaces the ability to prove business value, and this talk examines that gap through a practical evaluation lens. It follows an open-source evaluation framework called Higher and a real incident-response agent example to show how teams can decide whether to hire, train, or retire an agent before scaling it. The discussion focuses on evaluation and product decision-making rather than model architecture or vendor comparisons.

■ Human readiness and organizational change
- Human and habits: whether teams, tools, and data are ready to trust an agent
- Why, Impact, Intent: defining an agent's job description, scope, and ROI

■ Choosing the right solution
- Rules vs responses vs reasoning: matching task ambiguity to the appropriate automation level
- Guardrails and MCP boundaries: human-in-the-loop controls for high-risk actions

■ Evaluation metrics for probabilistic software
- Quantitative metrics such as edit distance, ping-pong rate, do-it-myself rate, instant resolution rate, and red-line rate
- Qualitative triage using deterministic telemetry, LLM classification, and unsupervised topic modeling
- Tone and human feedback as signals for product fixes and adoption

■ Practical implementation
- An incident-response SRE agent case and a GitHub repository with guidance, metric definitions, and a sample analytics workflow

Viewers responsible for AI product strategy, analytics, or engineering management gain a structured way to assess agent value, safety, and adoption. The talk is suited to teams planning or already deploying AI agents and offers an action plan: test the agent idea against the Higher framework, define measurable outcomes, and use the shared repository to prototype evaluation before production.