AI-Augmented Microservices Design - Agent friendly APIs - Full day
AI agents and LLMs are no longer side clients—they are becoming primary API consumers. Traditional API design isn’t enough. APIs must now be machine-parsable, agent-aware, retry-safe, and resilient under autonomous orchestration.
This full-day workshop gives you a step-by-step playbook to design APIs that are:
Discoverable (introspectable contracts, JSON-LD hints).
Secure (agent-specific guardrails, rate limits, prompt-injection protection).
Resilient (idempotency, retry safety, observability).
Domain-ready (patterns for e-commerce, healthcare, insurance, and manufacturing).
Audience: API & Platform Architects, Backend Engineers, Cloud-native teams, Product API leaders
Outcomes: Participants leave with 10 AI themes + checklists for building agent-ready, enterprise-resilient APIs.
Module 1 – Foundations of AI-Ready Microservices
- AI as the New API Consumer → Why APIs must evolve.
- From Human-Friendly to Machine-Parsable → Contracts, schemas, and discoverability.
Module 2 – Designing Agent-Aware Endpoints
- Guardrails for Intelligent Agents → Policy as code, abuse prevention, prompt-injection safety.
- Idempotency as a Survival Skill → Retry safety and compensations.
Module 3 – Security & Observability in the Age of AI
- Security for Human vs Agent Clients → Auth strategies, scopes, and quotas.
- Observability for AI Traffic → Tagging, tracing, anomaly detection, effectiveness metrics.
Module 4 – Domain-Specific AI Microservice Patterns
- Domain-Aware AI Patterns →
- E-commerce: Carts, orders, inventory.
- Manufacturing: IoT ingestion, MES/ERP integration.
- Insurance: Claims, underwriting, explainability.
- Healthcare: FHIR APIs, HIPAA compliance, consent.
Module 5 – Lifecycle & Tooling for AI-Integrated APIs
- Evolving API Contracts for AI Lifecycles → Versioning, CDC testing, deprecation policies.
- LLM Toolchain Integrations → OpenAI function calling, LangChain, Bedrock Agents.
Module 6 – Resilience & Enterprise-Scale Adoption
- Enterprise Resilience in the Age of AI → Bulkheads, degraded modes, regional failover.
- Maturity model for AI-augmented APIs.
- End-to-end architecture example.
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.