Architecting Microservices for Agentic AI Integration
Autonomous LLM agents don’t just call APIs — they plan, retry, chain, and orchestrate across multiple services.
That fundamentally changes how we architect microservices, define boundaries, and operate distributed systems.
This session delivers a practical architecture playbook for Agentic AI integration — showing how to evolve from simple request/response designs to resilient, event-driven systems.
You’ll learn how to handle retry storms, contain failures with circuit breakers and bulkheads, implement sagas and outbox patterns for correctness, and version APIs safely for long-lived agents.
You’ll leave with reference patterns, guardrails, and operational KPIs to integrate agents confidently—without breaking production systems.
Problems Solved
- Microservices collapse under agent retries or fan-out behavior
- Lack of event logs or compensations breaks agent re-planning
- Failures cascade due to missing bulkheads or circuit breakers
- Non-deterministic APIs cause unpredictable agent actions
- Ops teams can’t separate or monitor agent vs human traffic
Why Now
- Agentic frameworks (Agentforce, LangGraph, CrewAI) are entering production.
- Traditional microservices assume human or synchronous clients — not autonomous retriers.
- Reliability, determinism, and observability must now be built into API contracts.
- Agent traffic adds new stress patterns and compliance visibility requirements.
What Is Agentic AI in Microservices
- Agents plan, retry, and chain service calls — requiring deterministic, idempotent APIs.
- Services must be tool-callable (stable operationId, strict input/output schemas).
- Systems must survive retry storms, fan-out, and long-lived sessions.
Agenda
Opening: The Shift to Agent-Driven Systems
How autonomous agents change microservice assumptions.
Why request/response architectures fail when faced with planning, chaining, and self-healing agents.
Pattern 1: Event-Driven Flows Use events, queues, and replay-safe designs to decouple agents from synchronous APIs. Patterns: pub/sub, event sourcing, and replay-idempotency.
Pattern 2: Saga and Outbox Patterns Manage long workflows with compensations. Ensure atomicity and reliability between DB and event bus. Outbox → reliable publish; Saga → rollback on failure.
Pattern 3: Circuit Breakers and Bulkheads Contain agent-triggered failure storms. Apply timeout, retry, and fallback policies per domain. Prevent blast-radius amplification across services.
Pattern 4: Service Boundary Design Shape services around tasks and domains — not low-level entities. Example: ReserveInventory, ScheduleAppointment, SubmitClaim. Responses must return reason codes + next actions for agent clarity. Avoid polymorphic or shape-shifting payloads.
Pattern 5: Integrating Agent Frameworks Connect LLM frameworks (Agentforce, LangGraph) safely to services. Use operationId as the agent tool name; enforce strict schemas. Supervisor/planner checks between steps. Asynchronous jobs: job IDs, progress endpoints, webhooks.
Pattern 6: Infrastructure and Operations
- Observability: Tag agent runs (x-agent-run-id), trace retries, success/failure.
- Versioning: Use SemVer, deprecation headers, and multi-version gateways.
- Resilience: Autoscale on retry rate, degrade gracefully, and run failover drills.
Wrap-Up: KPIs and Guardrails for Production Key metrics: retry rate, success ratio, agent throughput, event replay lag. Lifecycle governance: monitoring, versioning, deprecation, and sunset plans.
Key Framework References
- Salesforce Agentforce – agentic orchestration and guardrail templates
- LangGraph / CrewAI – multi-agent planning and coordination patterns
- Cloud Native Patterns: Saga, Outbox, Circuit Breaker, Bulkhead, Event-Driven Architecture
- OpenTelemetry + Prometheus: Observability for agent vs human traffic
- OWASP LLM Top-10: Guardrails for safe function calling and data handling
Takeaways
- Blueprint for agent-friendly microservices architecture
- Patterns for event-driven, saga, and outbox consistency
- Guardrails: circuit breakers, bulkheads, least privilege APIs
- Framework integration checklist (Agentforce, LangGraph, etc.)
- Ops playbook for observability, versioning, and resilience
- KPIs to measure readiness: retry rate, grounding accuracy, and agent success ratio
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
Rohit loves to connect on http://www.productivecloudinnovation.com.
http://linkedin.com/in/rohit-bhardwaj-cloud or using Twitter at rbhardwaj1.