Building a Production-Ready Agent with Amazon Bedrock AgentCore + Strands Agents
Organizations today need scalable, secure, and flexible automation to handle complex business processes, particularly in regulated environments like compliance checking and user workflows. By combining Amazon Bedrock AgentCore with Strands Agents, it’s possible to build an AI-powered agent that:
- Integrates seamlessly with existing systems (CRM, databases, APIs)
- Executes tool-enabled workflows
- Maintains persistent memory and state
- Scales to production without managing infrastructure
The CloudContactAI A2P Compliance Agent Backend demonstrates this approach in a real, production-oriented project.
Background & Challenge
Use Case
Autonomous backend agent to enforce or verify compliance (TCPA, FDCPA) for communications (SMS, email) without manual review.
Key Challenges
- Scalable, secure runtime to handle dynamic agent behavior
- Integration with existing data stores and logs
- Persistent memory for context across sessions (compliance history, decisions)
- Tool execution — API calls, data lookups, and workflows
- Minimal infrastructure overhead — avoiding traditional custom frameworks
Building a robust “smart backend agent” is complex. Using a managed agent runtime plus modular framework simplifies this process significantly.
Solution Architecture
Components & Roles
| Layer / Component | Function / Role | Implementation / Notes |
|---|---|---|
| Agent Runtime | Serverless runtime + orchestration; handles scaling, isolation, secure execution | Amazon Bedrock AgentCore Runtime |
| Agent Framework | High-level abstraction for defining agents, tools, and workflows | Strands Agents SDK |
| Tools / Integrations | Utilities for database access, API calls, compliance logic, workflow orchestration, logging | CloudContactAI backend (e.g., compliance_strand.py, compliance_pipeline.py) |
| Memory / Persistence | Store agent state, session history, compliance records, logs | AgentCore Memory (DynamoDB or other persistent store) |
| API / Interface Layer | REST API or webhooks for triggering agent logic | api.py exposes /compliance/check and /compliance/batch |
| Observability / Logging | Monitor performance, failures, alerting, audit trails | AgentCore Observability (CloudWatch) + custom logs (cloudwatch_logger.py) |
High-Level Workflow
- External trigger: e.g., new SMS/email, or API call from CRM
- Request hits agent API (/compliance/check or /batch)
- AgentCore instantiates a Strands Agent in the secure runtime
- Agent runs compliance logic via tools, rules, and memory
- Agent returns a structured response: compliant | non-compliant | requires review, violations, recommendations, confidence score
- Upstream systems act on response (block send, queue for review, log audit)
- All events logged, state persisted, metrics captured
The architecture decouples agent logic from infrastructure, ensuring reusability, auditability, and scalability.
Implementation — CloudContactAI Compliance Agent Backend
Here’s how the CloudContactAI team translated the architecture into a real, production-ready implementation, with code, deployment steps, and key modules explained.
Setup & Dependencies
- Python 3 environment + requirements.txt
- AWS account with permissions to deploy AgentCore agents
- Infrastructure defined via Terraform for reproducible setup
Key Files & Roles
| File / Module | Purpose |
|---|---|
| agent_core.py | Core compliance decision engine and rule evaluation |
| compliance_strand.py | Strands Agent interface wrapping compliance logic for execution |
| api.py | REST endpoints for external triggers (single/batch compliance checks) |
| compliance_pipeline.py | Example pipeline orchestration for batching, validation |
| cloudwatch_logger.py | Logging, error tracking, and metrics integration with CloudWatch |
| deploy.yaml / Dockerfile | IaC and container config for production deployment |
Running & Deployment
- Install dependencies and activate virtual environment
- Test locally: python3 example_usage.py
- Start REST server via api.py for webhook/integration
- For production: containerize and deploy via AgentCore runtime; it handles scaling, session isolation, and security
Results & Benefits
Using this architecture (AgentCore + Strands + Compliance Agent) yields several concrete benefits:
- Fast time-to-production — by using managed AgentCore and reusable Strands framework. No need to build infrastructure from scratch.
- Flexibility & Modularity — agent logic decoupled from tool & infra, easy to update compliance rules, add new tools, integrate with other systems.
- Scalability & Security — serverless runtime handles scale, sessions are isolated, secure sandboxing, enterprise-grade AWS-level security and IAM integration.
- Persistence & Memory — can maintain conversation context, compliance history, data across sessions. Useful for audits, repeated checks, or multi-step workflows.
- Clear observability and audit support — Detailed logs, performance metrics, and error tracking through CloudWatch and OpenTelemetry give teams the visibility they need for monitoring and compliance reviews.
- Easy integration with existing systems — The REST API interface allows CRMs, backend apps, and automation pipelines to use the agent directly without changing their current platforms.
Why Amazon Bedrock AgentCore + Strands Agents Is a Strong Choice
Works with any framework
AgentCore doesn’t force you into a single way of building agents. You can switch tools or frameworks over time without being locked into a closed system.
Flexible, tool-friendly structure with Strands Agents
Strands makes it straightforward to set up tools, add memory, design workflows, and even let multiple agents work together. It also supports orchestration through the MCP protocol, which helps when an agent needs to use several tools in one flow.
Handles long and complex sessions
With support for sessions that run for many hours, AgentCore can take care of detailed, multi-step tasks that involve repeated decisions or branching logic.
Security and visibility built in from the start
Identity controls, access permissions, logging, and monitoring come pre-integrated. This removes the workload of creating your own security and observability layers and helps teams focus on the actual agent logic.
Lessons Learned & Best Practices
- Keep agent logic modular and isolated — Separate business rules like compliance logic into independent modules to simplify updates and maintenance without affecting infrastructure.
- Use persistent memory wisely — Store session data, historical decisions, and logs to enable auditing, traceability, and continuity across multiple agent interactions.
- Expose agent via API — Decoupling from the UI allows CRMs, pipelines, or other backend services to trigger agent logic programmatically and reliably.
- Monitor performance and metrics early — Track latency, errors, usage, and resource consumption from the start to facilitate debugging and ensure scalable operations.
- Use proper IAM roles and least-privilege access — Limit agent access to only necessary resources when integrating with databases, APIs, or executing workflows.
- Sandbox tool and code execution — Run any code or tool actions in secure sandboxes or isolated runtimes to prevent security risks or accidental system changes.
Potential Extensions & Use Cases
Beyond compliance checking, this modular and framework-agnostic architecture can be adapted for a wide variety of AI agent applications:
Automated Customer Support Agents
Handle multi-step workflows with tool access, memory, and context-aware interactions, reducing manual support efforts.
CRM Workflow Automation
Automate tasks such as lead scoring, contact enrichment, and email/SMS follow-ups to streamline sales and marketing processes.
Data Validation and Enrichment Agents
Validate external data, cross-check databases, and format information automatically to ensure high-quality, consistent datasets.
Monitoring & Alerting Agents
Continuously track logs and system health, automatically sending notifications via SMS, email, or collaboration tools like Slack.
Document Processing and RAG Workflows
Parse, analyze, and extract insights from documents; perform retrieval-augmented generation to answer questions and summarize content.
Because the system is modular and framework-agnostic, these agents can be extended to many domains beyond compliance, including customer service, operations, and analytics.
Conclusion
Combining Amazon Bedrock AgentCore with Strands Agents provides a production-ready, scalable, and secure foundation for building tool-enabled AI agents. The CloudContactAI Compliance Agent Backend showcases how to implement, deploy, and scale sophisticated agent logic without the burden of custom infrastructure.
If your project requires AI agents with persistent memory, tool access, workflow orchestration, and integration with existing systems, this architecture is a strong candidate.
Interested in building your own agent with AgentCore and Strands?
Connect with us to review the codebase, explore best practices, or collaborate on custom agent workflows.