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.
---
Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
Live updates related to this session LIVE
Sourced via Parallel AI Monitor — continuous web watch on 21 topical streams. Updated .
- microsoft.com high confidence Agent governance & policy gating
Microsoft Build 2026: Securing code, agents, and models ...
Google Cloud released a guide for GitOps-Secured AI Workspaces utilizing the GKE Agent Sandbox (powered by gVisor). This implementation provides kernel-level isolation to intercept system calls and uses Kubernetes Network Policies to enforce strict network boundaries, preventing
- forbes.com high confidence Agent-native data infrastructure
Oracle AI Database Agentic AI: Enterprise Features Address Data ...
An agentic access-aware RAG implementation using Amazon FSx for NetApp ONTAP enables agents to retrieve enterprise data while respecting per-user and per-file access permissions (agent-aware access control).
- devflokers.com high confidence Agent safety & prompt injection
Open-Source AI June 2026: New Models, Agents & Papers
Research/Development Update: Berkeley RDI's 'Agentic AI Weekly' (June 24, 2026) highlighted the release of OpenAI's GPT-5.5-Cyber model and its performance progress on 'CyberGym', indicating advancements in AI agents' cybersecurity capabilities and autonomous operation in cyber-e
- arkoselabs.com high confidence Agent safety & prompt injection
2026 Agentic AI Security Report - Arkose Labs
Research/Development Update: Berkeley RDI's 'Agentic AI Weekly' (June 24, 2026) highlighted the release of OpenAI's GPT-5.5-Cyber model and its performance progress on 'CyberGym', indicating advancements in AI agents' cybersecurity capabilities and autonomous operation in cyber-e
- businesswire.com high confidence Agent-native data infrastructure
Unravel Data Unveils Arvix AI, the First Agentic AI Engine ...
Linux Foundation announced its intent to launch the 'Agent Name Service' to establish a trusted identity infrastructure for AI agents, providing a foundational layer for agent identification and trust in agentic ecosystems.
External links matched to this session via topic relevance. The KB does not endorse third-party content; verify before citing.