Beyond APIs: Orchestration Patterns & MCP for Multi-Agent Systems
Enterprises are moving from single AI agents to networks of agents that trigger thousands of API calls, retries, and tool-chains per prompt. Without orchestration discipline and APIs built for AI-scale, systems buckle under bursty load, retry storms, cache-miss spikes, inconsistent decisions, and runaway costs.
This talk shows how to combine MCP (Model Context Protocol) with proven inter-agent orchestration patterns — Supervisor, Pub/Sub, Blackboard, Capability Router — and how to harden APIs for autonomous traffic using rate limits, dedupe, backpressure, async workflows, resilient caching, and autoscaling without bill shock.
You’ll also learn the AIRLOCK Framework for governing multi-agent behavior with access boundaries, identity checks, rate controls, least-privilege routing, observability, compliance filters, and kill-switches.
You will walk away with a practical blueprint for building multi-agent systems that are fast, safe, reliable, and cost-predictable.
KEY TAKEAWAYS
Pattern Literacy: When to use Orchestrator, Pub/Sub, Blackboard, Router
MCP Fluency: Standardize agent↔tool integration
API Scaling: Rate limits, dedupe, backpressure, async, caching
Resilience: Bulkheads, jitter, circuit breakers, autoscaling guardrails
Observability: Trace chain-ID/tool-ID across agents & tools
AIRLOCK Governance: Access boundaries, identity, rate controls, least-privilege routing, compliance, kill-switches
AGENDA
Why AI Changes Load Patterns
Bursty workloads · fan-out · retry amplification · cost spikesMCP 101
Standardized agent→tool access · hot-swappable toolsOrchestration Patterns
Supervisor · Pub/Sub · Blackboard · Capability RouterArchitecting APIs for AI Traffic
Multi-dimensional rate limits · dedupe · backpressure · SWR caching · asyncResilience & Autoscaling
Circuit breakers · bulkheads · kill-switches · budget capsObservability & Governance
Chain-ID tracing · anomaly detection · AIRLOCK boundaries
Zero fluff.