If you didn’t have a chance to attend AWS re:Invent this year, don’t worry. While key sessions will be available online, here is a concise summary of one of the standout sessions I attended at #reInvent2025.
All credit to AWS and the presenters of this session.
“Agents in Enterprise: Best Practices With Amazon Bedrock AgentCore”
Moving from POC 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. Define what the agent should and shouldn't do, with clear and complete definitions and expected.
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.
Reducing ambiguity reduces tokens and costs. Leverage existing MCP servers and expose tools via the MCP server and show integration patterns with code samples.
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.
Monitor how agents collaborate and escalate tasks.
6. Establish Proper Memory Boundaries
Plan for:
•short-term session memory
•long-term personalised memory
Isolate 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 actually require reasoning rather than forcing agents into everything.
8. Test Relentlessly
Rerun evaluation after every update.
Use:
• A/B deployments
• drift monitoring
• automated rollback
Production monitoring is not optional, it’s mandatory.
9. Scale Through Platform Standardisation
Deploying agents to production is step one, not the finish line.
To scale safely:
•Build a central platform team for enablement
•Standardise governance, observability, and tooling
•Promote cross-team collaboration to avoid duplicated effort
The session showcased an excellent org model outlining split responsibilities between platform vs. use-case teams.
So 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 & context injection.
- MCP & A2A Compatible: Seamless interoperability between agents and MCP servers
- Memory Layer: Persistent and session-based memory for personalisation.
- Tooling: Catalog + governance + reuse capability. Define MCP servers, use AgentCore Browser Tooling for safe web navigation and data extraction. And 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 & compliance guardrails.
- Evaluation Engine: Built-in testing and performance assessment with customisable metrics.
Final Takeaway
This session perfectly reinforced that building agents is not just about prompting, it’s about engineering:
• platform standardisation
• tooling governance
• secure orchestration
• memory boundaries
• rigorous evaluation
• enterprise scalability
AgentCore becomes the backbone that enables all of this, from experimentation to full-scale production with observability, governance, and operational safety built in.
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