AWS re:Invent 2025 - Bridging from POC to production: An intro to Amazon Bedrock AgentCore (AIM2204)

Bridging the Gap from Prototype to Production: An Introduction to Amazon Bedrock AgentCore

Challenges in Transitioning AI Agents from Proof-of-Concept to Production

• Developing basic AI agents is relatively easy using frameworks like LangChain, Strand Agents, and Crew AI
• However, moving these prototypes to production with thousands of users, reliability requirements, and compliance needs is a completely different challenge
• Key challenges include:
  1. Performance: Ensuring agents are responsive, reliable, and accurate at scale, with proper memory management and continuous evaluation
  2. Scalability: Building a secure, resilient, and elastic runtime that can handle thousands of concurrent agent sessions
  3. Security: Implementing fine-grained identity and access controls for each agent session
  4. Governance: Providing visibility and real-time control over agent actions to enforce business rules

Amazon Bedrock AgentCore: A Platform for Building, Deploying, and Operating Agents at Scale

• Amazon Bedrock AgentCore is a fully managed service that provides a complete set of capabilities to address the challenges of transitioning agents to production
• Key characteristics of AgentCore:
  1. Time to Value: Serverless architecture with pay-per-use pricing, eliminating infrastructure management overhead
  2. Flexibility: Modular services that work with any framework, model, or protocol (e.g., MCP, A2A)
  3. Enterprise Readiness: Built-in controls, access management, and observability for secure, reliable, and auditable agent deployments

Key AgentCore Services

1. Memory: Automatically extracts and stores short-term and long-term memory to enable context-aware agents
2. Gateway: Composes tools and agents across an ecosystem into a single, secure MCP interface
3. Runtime: Secure, serverless environment to host agents and tools, with built-in session isolation and scalability
4. Identity: Simplifies agent identity and access management, allowing agents to securely access services on behalf of users
5. Policy: Provides real-time, deterministic control over agent interactions with enterprise tools and data
6. Observability: Offers rich visualization and open telemetry compatibility for agent workflows and operational metrics
7. Evaluations: Continuously inspects agent behavior to catch issues before they cause widespread impact

Ericsson's Use Case: Accelerating Network Engineer Productivity with AgentCore

• Ericsson, a global leader in telecommunications infrastructure, built an agentic solution to reduce network engineers' research time from days to minutes
• Key components:
  1. Specialized Agents: Experts in areas like telecom standards, code generation, and system design
  2. Knowledge Layer: Unifies data from industry standards, codebase, and product documentation to provide a system-level understanding
  3. Orchestration Layer: Coordinates the collaboration between specialized agents to deliver comprehensive insights
• Benefits:
  1. 99% reduction in research time for network engineers, from 3-5 days to less than 7 minutes
  2. Improved accuracy and reliability by involving domain experts and unifying knowledge sources
  3. Faster development cycles by leveraging AgentCore's managed infrastructure and services

Key Takeaways

1. Business value only comes when agents are in production, but the transition from prototype to production is a significant challenge.
2. Observability, security, and scalability are essential for deploying mission-critical agents.
3. Amazon Bedrock AgentCore provides a comprehensive platform to address the challenges of moving agents to production, with modular services and enterprise-grade capabilities.
4. Ericsson's use case demonstrates how AgentCore can accelerate the development and deployment of agentic solutions, delivering tangible business impact.