In this code-first session, we demonstrate how to add agentic AI capabilities and augment a legacy application using Amazon Bedrock AgentCore and the Amazon Strands Agents SDK. We will explore how to build AI-powered features for a legacy application without modifying the existing backend code. We will showcase how to leverage existing APIs and Lambda functions as the backbone for your agentic AI experience. You'll learn how to execute code in isolated sandbox environments, ensuring security while accessing internal data sources with Amazon Bedrock AgentCore Code Interpreter.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
You don't need to rewrite a legacy app to make it agentic. Wrap it. Use existing APIs and Lambda functions as the agent's tools; run untrusted code in sandboxed Code Interpreter environments.
Why it matters
Most enterprise IP lives in legacy code. Rewriting kills you. Wrapping unlocks AI value without the rewrite tax.
The hard parts
Legacy APIs lack the granularity agents need (too coarse, idempotency unclear, error handling inconsistent). Auth boundaries don't match agent needs.
Playbook moves
(1) Inventory existing APIs; score them for agent suitability — idempotency, granularity, auth context, error semantics. (2) Build a thin agent-API adapter layer rather than expose internals. (3) Use Code Interpreter sandboxes for any agent that needs to compute, not call.
The surprise
The Code Interpreter sandbox is the safest pattern most teams ignore. It lets you give agents *capability* without giving them *prod access*. Sandbox + result-passing handles 80% of the "agent needs to run code" problem with a fraction of the blast radius.
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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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