Optimizing agent system prompts with Amazon Bedrock AgentCore
AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer's reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors.
Amazon Bedrock AgentCore offers optimization features to improve agent quality. Traditionally, enhancing low-scoring agents required manual review of long traces to pinpoint issues, tweaking components like prompts and skills, and retesting. AgentCore optimization streamlines this through production traces, proposing configuration changes, validating via batch evaluation and A/B testing, and promoting the best results.
The system prompt optimizer in AgentCore utilizes agent traces and a reward signal to generate improved system prompts. For instance, in the market trends agent, it contrasts an existing system prompt with a revised version, explaining the trace patterns that guided the changes.
The system prompt optimizer operates through an agentic reflector, which reviews evaluated agent behavior, identifies patterns distinguishing success from failure, and suggests targeted configuration modifications. Agent traces, though lengthy, are processed by a filesystem for the reflector rather than being truncated. The reflector inspects the trace corpus, listing files, searching with grep, reading traces with cat, comparing outputs with diff, and selectively examining successful and unsuccessful runs.
It determines relevant evidence, makes comparisons, and translates findings into configuration changes before passing through platform-level guardrails for review and testing.
The Single Agent Reflector is currently used for system prompt recommendations in AgentCore optimization, analyzing the full trace set in a single pass and returning a coherent set of edits. In contrast, the Sub-Agent Reflector employs a swarm of agents to explore the trace set dynamically, focusing on patterns in the first few traces while missing failure modes in other portions.
The orchestrator aggregates findings from sub-agents, generalizes recurring patterns, removes duplicates, and condenses insights into configuration changes.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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