Discover how to build powerful AI agents using Amazon Bedrock's suite of tools, with a focus on Amazon Bedrock AgentCore. This session explores how Parrot Analytics leveraged the modular components of Amazon Bedrock AgentCore and Amazon Nova foundational models to achieve 10x the processing speed of manual classification across 2M+ entities. We will dive into prompt and context engineering, knowledge bases, and observability for production agentic workloads.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Modular AgentCore components + Nova foundation models for high-throughput entity classification (10× speedup over manual on 2M+ entities).
Why it matters
Classification at scale is a sweet spot for agents — well-defined output, clear evals, repetitive task. If your business has classification as a bottleneck, this pattern works.
The hard parts
Prompt drift over millions of entities. Evals become statistical, not anecdotal. Inter-rater agreement (when humans review agent output) becomes a meaningful metric.
Playbook moves
(1) Stratified sampling for evals — don't just look at random 100. (2) Re-eval on every prompt change. (3) Track inter-rater agreement between agent and humans over time.
The surprise
Modular AgentCore decomposition lets you swap models per stage. Use a cheap model for triage ("is this even worth processing?"), a mid-tier for the bulk, and an expensive model only for ambiguous cases that fail confidence checks. Don't run uniform inference. The cost difference is 10×.
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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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