Building Enterprise-Ready AI Agents: Key Takeaways from AWS re:Invent 2025

Source: Dev.to

Introduction

If you didn’t have a chance to attend AWS re:Invent 2025, don’t worry. While key sessions will be available online, here is a concise summary of one of the standout sessions: “Agents in Enterprise: Best Practices With Amazon Bedrock AgentCore.”

Moving from proof‑of‑concept to production with AI agents is rarely straightforward. Challenges arise around accuracy, scalability, latency, infrastructure costs, model inference expenses, security, observability, and memory retention. Many teams jump straight into building agents without planning where to start and how to operationalize an agentic platform at enterprise scale. This session distilled nine core best practices for building robust, production‑ready agentic systems.

Top 9 Best Practices for Agentic Platform Success

1. Start Small & Work Backwards

Agent development is an interactive journey. You can adopt new models, add tools, and improve prompts over time. Define what the agent should and shouldn’t do with clear, complete definitions and expected outcomes.

2. Implement Observability from Day One

Agents are OTEL‑compatible. Enable full trace‑level visibility and observability dashboards early, not later.

3. Define Your Tooling Strategy Explicitly

Document tool requirements, input/output schemas, and error‑handling logic.

4. Automate Evaluation

Define technical and business metrics early and include business users in the evaluation loop. Test across diverse user intents, including misuse patterns, to strengthen resilience.

5. Avoid the “One Agent With 100 Tools” Anti‑Pattern

Use multi‑agent architectures with clear roles, orchestrated workflows, and shared context.

6. Establish Proper Memory Boundaries

Plan for isolation of user context and enforce security policies at execution. Host agents and tools separately for compliance and performance.

7. Cost vs. Value: Be Pragmatic

If deterministic code works reliably, use it. Reserve agent reasoning for tasks that truly require reasoning rather than forcing agents into everything.

8. Test Relentlessly

Rerun evaluation after every update. Production monitoring is not optional—it’s mandatory.

9. Scale Through Platform Standardisation

Deploying agents to production is step one, not the finish line. Standardise platforms to enable consistent scaling.

The session also showcased an organizational model that splits responsibilities between platform and use‑case teams.

Where Does AgentCore Fit In?

Amazon Bedrock AgentCore operationalises these best practices out‑of‑the‑box, enabling enterprise‑grade agent development at scale.

Key Capabilities Overview

• Runtime – Supports any agent framework, prompt schema, tool routing, and context injection.
• MCP & A2A Compatibility – Seamless interoperability between agents and MCP servers.
• Memory Layer – Persistent and session‑based memory for personalisation.
• Tooling – Catalog, governance, and reuse capabilities. Define MCP servers, use AgentCore Browser Tooling for safe web navigation and data extraction, and a Code Interpreter to execute code securely in isolation when needed.
• Identity & Access Control – Ensures the right agent accesses the right tool securely.
• Policy Enforcement – Applies organisational rules and compliance guardrails.
• Evaluation Engine – Built‑in testing and performance assessment with customisable metrics.

Final Takeaway

Building agents is not just about prompting; it’s about engineering. AgentCore becomes the backbone that enables everything from experimentation to full‑scale production, with observability, governance, and operational safety built in.