Gain deep visibility into the performance and reliability of autonomous agents with Amazon CloudWatch. This session showcases how CloudWatch delivers endtoend observability for agentic AI workloadstracking decision quality, token efficiency, and workflow execution at scale. Explore prebuilt dashboards and advanced metrics that help you optimize agent performance, control operational costs, and maintain consistent behavior across complex intelligent systems. Walk away ready to implement productiongrade observability that ensures your AI agents operate reliably, make optimal decisions, and deliver measurable outcomes at scale.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Beyond logs and metrics — observability for agent decisions, token efficiency, multi-step workflow execution, and decision drift over time.
Why it matters
Agents are stateful, multi-step, and expensive. Traditional APM misses the cost dimension entirely and treats decision quality as opaque.
The hard parts
Defining the right metrics is non-obvious. "Request rate" doesn't mean what it used to. "Decision quality" isn't directly measurable.
Playbook moves
(1) Track four axes minimum: cost per task, success rate, latency per task (end-to-end, not per LLM call), decision drift over time. (2) Build dashboards per agent, not per service. (3) Alert on cost spikes, not just error spikes.
The surprise
The metric most agentic systems should track and don't is *loop count* — how many tool calls per completed task. It's the canary for prompt regression, model drift, and broken tools. When loop count starts trending up week-over-week, something is wrong even if all your other metrics look fine.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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