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Course Overview and Foundations

Course Orientation and Objectives for Real-Time Agents

Real-Time Systems Fundamentals for Agent Design

Agent Architecture Patterns and Trade-offs

Data Ingestion and Streaming Feature Engineering

Live Data Ingestion and Protocols

Streaming Feature Extraction and State Management

Model Orchestration and Tool Use in Real Time

Low-Latency Inference, Retrieval, and Tool Invocation

Planning, Guardrails, and Safety for Real-Time Agents

Reliability, Scalability, and Cost Efficiency

Resilient and Scalable Real-Time Agent Infrastructure

Performance Engineering and Cost Controls

Operations, Observability, and Compliance

Production Operations for Real-Time Agents

Telemetry, Monitoring, and Evaluation Loops

Security and Governance

Security Architecture for Real-Time Agents

Data Privacy, Compliance, and Risk Management

Product Delivery and Cross-Functional Leadership

From Concept to Rollout: Planning and Roadmapping

Capstone: Architect and Implement a Real-Time Agent

Knowledge Checks and Quizzes

Quiz: Real-Time Fundamentals

Quiz: Agent Architecture Patterns

Quiz: Ingestion Protocols and Schemas

Quiz: Streaming Features and State

Quiz: Low-Latency Inference and Retrieval

Quiz: Safety and Guardrails

Quiz: Reliability and Scalability

Quiz: Performance and Cost Engineering

Quiz: Operations and Observability

Quiz: Security, Privacy, and Compliance

Final Survey

Course Feedback Survey

Designing and Operating Production-Grade Real-Time AI Agents is a hands-on, end-to-end course for engineers, architects, and technical leaders who need to build low-latency AI systems that are safe, reliable, and cost-efficient in production. You’ll learn how to translate product goals into latency budgets, design ingestion pipelines and streaming features, orchestrate models and tools with strict SLAs, and operate agents at scale with robust observability and governance. The curriculum blends real-time systems fundamentals with modern agent patterns—covering reactive loops, retrieval strategies, memory design, safety guardrails, and action authorization—so you can ship agents that perform under pressure. Beyond architecture, the course dives deep into reliability engineering, autoscaling, cost controls, and compliance for regulated environments. Each module includes practical checklists and templates you can apply immediately. You’ll put it all together in a capstone where you architect, implement, and load test a production-grade agent, then present results against SLOs. By the end, you’ll have the skills, artifacts, and confidence to lead cross-functional delivery and operate real-time AI agents in demanding, customer-facing scenarios.

Designing and Operating Production-Grade Real-Time AI Agents

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            <p>Real-time AI agents are crossing the threshold from demos to dependable production systems. Designing and operating them at scale requires a disciplined approach that blends low-latency systems engineering, robust agentic architectures, careful safety and governance, and pragmatic product leadership. This course establishes that foundation and guides you through the complete lifecycle—design, build, operate, lead—so you can ship agents that consistently meet strict latency budgets, withstand unpredictable traffic, respect privacy, and deliver measurable business outcomes.</p>

<p>The journey begins by grounding you in real-time systems fundamentals—latency, throughput, tail behavior, jitter—and mapping those concepts to modern agent loops that observe, reason, act, and maintain state. You will compare event-driven, request/response, and streaming paradigms; evaluate microservices versus monoliths; and analyze control-plane/data-plane separations, online/offline inference, hybrid retrieval, and memory designs. With these foundations, you will tackle live data ingestion at scale using WebSockets, gRPC streaming, SSE, and MQTT; define contracts with Protobuf, Avro, and JSON; and implement backpressure, ordering guarantees, idempotency, schema evolution, multi-tenant isolation, PII protections, and dead-letter strategies.</p>

<p>Mid-course, the focus shifts to low-latency model orchestration and tool use: allocating latency budgets across retrieval, models, and external tools; employing structured prompting, function calling, and adapters; and delivering partial results via streaming tokens. You will design planning and guardrail layers with constrained decoding, policy enforcement, red-teaming, and action authorization that respects rate limits and quotas—while maintaining compliance for streaming contexts. Reliability and performance are treated as first-class concerns through horizontal scaling, autoscaling triggers, queue smoothing, circuit breakers, jittered retries, idempotency keys, multi-region design, degradation strategies, P95/P99 optimization, cold-start mitigation, batching, caching embeddings, RAG shard design, and SLA-aware model selection with cost observability and adaptive throttling.</p>

<p>Operations and observability modules translate engineering into durable production practice. You will implement CI/CD for models and policies, canary and shadow deployments, SLOs/SLIs for latency and correctness, error budgets, and rigorous incident response with rollback and post-incident review. Deep telemetry—tracing the agent loop with correlation IDs and session state—enables live evals, replay testing, counterfactual analysis, drift detection, and continuous improvement. Security and governance threads span the entire course: AuthN/AuthZ with scoped tokens, secrets management, outbound call controls, prompt injection defenses, egress policies, data minimization and retention for streams, auditability, explainability, regulatory considerations, and vendor risk management.</p>

<p>The capstone consolidates these skills: you will define requirements and SLAs with a concrete latency budget; architect ingestion, memory, model orchestration, and actions; and implement observability before executing load tests and synthesizing results. Throughout, quizzes reinforce mastery of fundamentals, architecture, ingestion, streaming features, inference and retrieval, safety, reliability, performance, operations, and compliance. By the end, you will possess the technical fluency and leadership toolkit to plan, launch, and operate production-grade real-time AI agents with confidence and accountability.</p>
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