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

Rohit Bhardwaj

About Rohit Bhardwaj

Rohit Bhardwaj is a Director of AI & Data Architecture at Salesforce, where he focuses on enterprise AI, agentic systems, cloud-native architecture, distributed systems, data platforms, security, and large-scale transformation.

Over his career, Rohit has designed and led complex enterprise platforms across AWS, Google Cloud, microservices, real-time data, API ecosystems, resilient distributed systems, and AI-enabled architectures. His work increasingly focuses on the challenges enterprises face as software evolves from deterministic services to AI-native and agentic systems—particularly around reliability, governance, evidence, security, observability, cost, and safe autonomy.

Rohit is the author of System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems, published by Apress. The book presents a modern approach to system design covering scalability, distributed systems, AI architecture primitives, security, reliability, economics, agentic systems, and real-world architectures including e-commerce, ride sharing, payments, fraud detection, messaging, video streaming, file storage, and search. (Springer Link)

Book:
Amazon: https://a.co/d/09Zs1twa
Publisher / Springer Nature: https://link.springer.com/book/10.1007/979-8-8688-2782-2
O'Reilly: https://learning.oreilly.com/library/view/system-design-with/9798868827822/ 

Rohit is also an O’Reilly instructor and a frequent speaker at technology conferences including No Fluff Just Stuff, UberConf, GIDS, and other international events. His talks focus on practical architecture lessons from building and operating complex systems, including AI control planes, trusted agents, inference at scale, evidence-first RAG, AI security, distributed-system failure, and AI-era software architecture.

As a trusted advisor and architecture leader, Rohit works at the intersection of business strategy and deep technical architecture—helping teams translate complex business problems into scalable, resilient, secure, and economically sustainable systems.

Rohit holds an MBA in Corporate Entrepreneurship from Babson College and graduate-level education in Computer Science from Boston University and Harvard University.

Connect with Rohit:
LinkedIn: http://linkedin.com/in/rohit-bhardwaj-cloud
X / Twitter: @rbhardwaj1

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