Production AI Engineering: Building Enterprise-Grade AI Agents

Most AI agent tutorials stop at “it responds to a prompt.” This course starts where they stop.

You will engineer a system that blocks prompt injection in under 5ms, compresses context by 60% before the LLM call, detects its own quality regressions and improves automatically, scales on Kubernetes from zero to ten replicas without dropping a request, passes a SOC 2 audit, and handles a regional cloud outage in under 30 minutes — without human intervention.

The five agents you ship in the capstone are portfolio-ready. They demonstrate the layer of AI engineering that enterprise teams actually pay for — and that no LangChain tutorial has ever covered.

30 Lessons. 4 Modules. 5 Production Agents. Starts August 4, 2026.

Explore Lessons 1–7: Secure Agent Foundations. Continue your journey through the complete Production AI Engineering.

Throughout this course, you'll build five production-ready AI agents that gradually evolve into a complete enterprise AI platform. You'll engineer secure agent architectures, multi-agent collaboration, real-time streaming, self-improving LLMOps pipelines, and Kubernetes-native deployments with enterprise security, compliance, disaster recovery, and FinOps.

Every project solves a real production challenge using the same architectural patterns found in enterprise AI systems. By the end, you'll have a portfolio that demonstrates not just how to use AI—but how to engineer AI systems that organizations can confidently deploy at scale.

Agent 1 — Secure Customer Service Agent

Every Week 1 control in one request: injection blocked at Layer 4 in <5ms, RBAC enforced at Layer 3, semantic cache at Layer 2 reducing token cost by 40%, output scanned for API keys and PII before delivery.

Agent 2 — Multi-Agent Research System

SupervisorAgent fans out to three parallel WorkerAgent instances via asyncio.gather(). One worker failure does not cancel the others. Total latency = slowest single worker, not the sum.

Agent 3 — Streaming Analytics Agent

StreamingLLM → SSE endpoint → EventSource client. First token under 500ms. The X-Accel-Buffering: no header that makes nginx stop buffering your stream.

Agent 4 — Self-Healing LLMOps Agent

EvalHarness blocks deploys on regression. ABRouter routes 10% of live traffic to a new prompt variant. CircuitBreaker trips after 3 failures and rejects requests in microseconds. PromptOptimiser runs weekly and commits winners to version control.

Agent 5 — Enterprise Kubernetes Deployment

KEDA scales on agent_request_queue_depth, not CPU — before latency degrades, not after. SOC 2 audit trail with chain-hash tamper detection. HIPAA PHI detector blocks protected data at the perimeter. Route 53 failover completes in under 30 minutes. FinOps allocator bills teams for exactly what they consumed.

Backend engineers who have built agents that work in demos and want to understand why they fail at scale.

Platform engineers who need to deploy LLM workloads on Kubernetes with the same operational rigour as any other production service.

Tech leads who own the “we need SOC 2 compliance for our AI system” conversation and need to know what to build.

Founding engineers at AI startups where the next customer due diligence will ask about security architecture, compliance controls, and disaster recovery.

No frameworks until you understand the layer below. You implement the security perimeter before touching any orchestration library. You implement the token bucket before reading about rate-limiting middleware.

Real numbers, not analogies. 60% token reduction from context compression. 15 percentage point accuracy gain from multi-agent debate. 67% infrastructure cost reduction from KEDA scale-to-zero. Every number comes from the lesson that demonstrates it.

Failure first. Every module ends with a “what breaks without this” section. You learn the failure mode the lesson prevents before you learn how it prevents it.

Production checklist on every lesson. Not “nice to have” suggestions — the specific checks that get caught in SOC 2 audits, load tests, and security reviews.

By Phase 1: You can articulate the four-layer agent security model, explain why tool calls require a five-gate validation pipeline, and deploy a secure agent base with 8-signal observability.

By Phase 2: You can implement parallel multi-agent dispatch, achieve first-token-under-500ms streaming, build layered error recovery with a dead-letter queue, and reduce token costs by 60% through context compression.

By Phase 3: You can build a CI quality gate that blocks deployments on regression, run statistically-valid A/B experiments on live traffic, implement a self-healing circuit breaker, and close the LLMOps feedback loop automatically.

By Phase 4: You can deploy an AI agent on Kubernetes with zero-downtime rolling updates, queue-depth auto-scaling, SOC 2 audit controls, HIPAA compliance safeguards, multi-region disaster recovery, and FinOps cost allocation.

Security Architecture: 4-layer enforcement model, prompt injection detection (regex + structural analysis), RBAC with role inheritance, output scanning for secrets and PHI, sandboxed subprocess execution with import allowlist.

Memory & Cost: Conversation buffer, semantic similarity cache (55% hit rate → 40% cost reduction), vector store for cross-session recall, four-strategy context compressor (60% token reduction), real-time Prometheus cost dashboard.

Reliability Engineering: Token bucket rate limiting, four-class error taxonomy, exponential backoff with jitter, model fallback chain, dead-letter queue, three-state circuit breaker, SLOs with error budget tracking and automated runbooks.

LLMOps: Evaluation harness with CI gate, A/B testing with deterministic routing and statistical significance, prompt optimisation with full audit trail, multi-agent debate (+15pp accuracy), knowledge graph for compliance-queryable relationships, full continuous improvement pipeline.

Enterprise Infrastructure: Kubernetes Deployment + HPA + KEDA (queue-depth scaling, scale-to-zero), SOC 2 immutable audit trail with chain-hash tamper detection, HIPAA PHI detection and 15-minute auto-logoff, multi-region disaster recovery (RTO < 30 min, RPO < 5 min), FinOps cost allocation with team showback and 80%-threshold budget alerts.

• Python 3.11+

• Comfortable reading and writing async Python (asyncio, await)

• Basic Docker knowledge (build an image, run a container)

• No prior AI/agent experience required — but familiarity with API calls helps

• OpenAI API key optional — every lesson runs in stub mode without one

Lessons 1–7

The architecture that every production agent runs on. By Lesson 7 you have a complete, hardened agent base — security, tools, memory, rate limiting, observability, and sandboxed execution. This is the foundation Phases 2–4 build on without modification.

Lesson 1 — The 4-Layer Secure Agent Architecture

Every production agent enforces the same request path: Security Perimeter → Tool Orchestrator → Memory Manager → Core LLM. A request that fails Layer 4 never reaches Layer 1. You implement all four layers — including stubs for the ones Lessons 2–7 will fill — so the architecture is correct from Day 1.

Core types: SecurityContext, AgentRequest, AgentResponse

Lesson 2 — Tool Execution & Validation

Five gates, in order: registry lookup, Pydantic schema validation, RBAC permission check, sandboxed subprocess execution, output filtering. Miss gate 3 and an unpermissioned caller reaches your database tool. Miss gate 5 and your agent returns API keys in its responses.

Core classes: ToolDefinition, ToolOrchestrator

Lesson 3 — Memory Systems — Short-Term, Semantic Cache & Redis

Three layers with different jobs: ConversationBuffer keeps the last N turns in memory. SemanticCache returns cached responses to near-identical queries using cosine similarity — 55% hit rate in production means 40% cost reduction without touching a single prompt. RedisBackedMemory persists state across restarts and enables cross-session recall.

Core classes: ConversationBuffer, SemanticCache, RedisBackedMemory

Lesson 4 — Rate Limiting & Cost Control

The token bucket algorithm: capacity sets the burst ceiling, refill_rate sets the sustained limit. A request with a projected cost above PER_REQUEST_CAP is rejected before the LLM call. A user who crosses DAILY_BUDGET_USD is blocked until midnight. This is what prevents the $40,000 API bill.

Core classes: Bucket, RateLimiter, CostTracker

Lesson 5 — Security Hardening — Secrets Scanning, HMAC Signing & Output Guard

SecretsScanner detects API keys, tokens, and credentials in both inputs and outputs using pattern matching and entropy analysis. HMACSigner signs every tool call so tampered requests are rejected before execution. OutputGuard scans every response for SSNs, card numbers, and filesystem paths before the response leaves the agent. Core classes: SecretsScanner, HMACSigner, OutputGuard

Lesson 6 — Observability Stack — Metrics, Structured Logs & Distributed Traces

Eight production signals: four golden (traffic, latency, errors, saturation) and four AI-specific (cost per request, cache hit rate, security rejection rate, tool execution latency). Every layer boundary emits a structured JSON log line with request_id. Four Grafana alert rules. One Telemetry class that wires all three pillars.

Core class: Telemetry

Lesson 7 — Sandboxed Code Execution — Isolated, Resource-Capped & Audited

SandboxExecutor spawns a subprocess, enforces RLIMIT_AS memory cap, kills on timeout, applies an import allowlist, and rejects any non-JSON output. SandboxAuditLog writes a tamper-evident record of every execution — args are hashed, never stored raw. An open import list is a filesystem deletion waiting to happen.

Core classes: SandboxPolicy, SandboxExecutor, SandboxAuditLog

Lessons 8–14

Patterns that make the Phase 1 foundation scale: parallel agents, streaming, layered error recovery, token compression, and production monitoring. By Lesson 14 the agent handles real concurrent load, streams output to the browser, recovers from failures without human intervention, and has a real-time dashboard showing spend per user per minute.

Lesson 8 — Multi-Agent Orchestration — The Supervisor Pattern

SupervisorAgent dispatches to N WorkerAgent instances via asyncio.gather(). Total wall-clock time = slowest worker, not the sum. A failed worker returns a WorkerResult with success=False — the supervisor logs it, excludes it from the response, and returns the rest. One worker going down does not take the request down.

Core classes: WorkerTask, WorkerResult, WorkerAgent, SupervisorAgent

Lesson 9 — ReAct Loop — Reasoning, Acting & Observing

ReActAgent alternates between Thought, Action, and Observation steps until the task is complete or the step budget is exhausted. Each Action calls a real tool; each Observation feeds the result back into the next Thought. The step budget is not optional — an uncapped ReAct loop is an infinite billing event.

Core classes: ReActStep, ReActAgent, StepBudget

Lesson 10 — Prompt Engineering — Templates, Versioning & Injection-Safe Construction

PromptTemplate separates static structure from dynamic inputs, enforces variable validation before assembly, and stores a version hash so every LLM call is traceable to the exact prompt that produced it. Prompt construction is where injection enters — building prompts by string concatenation is the root cause.

Core classes: PromptTemplate, PromptRegistry, PromptVersion

Lesson 11 — Structured Output — Schema Enforcement & Retry-on-Failure

StructuredOutputParser validates every LLM response against a Pydantic schema before it reaches application code. On a parse failure, OutputRetryHandler regenerates with the error message injected into the prompt — up to max_retries attempts. An unvalidated LLM output reaching a database write is a data corruption event, not an edge case.

Core classes: StructuredOutputParser, OutputRetryHandler

Lesson 12 — Agent State Machine — Lifecycle, Transitions & Persistence

AgentStateMachine enforces valid state transitions: IDLE → RUNNING → WAITING_FOR_TOOL → RUNNING → COMPLETE | FAILED. An invalid transition raises immediately — silent state corruption is harder to debug than a loud exception. State snapshots are persisted to Redis so a pod restart does not lose in-progress agent work. Core classes: AgentState, AgentStateMachine, StateSnapshot

Lesson 13 — Async Agent Pipeline — Concurrent Execution & Backpressure

Three sequential tool calls at 1,500ms become three parallel calls at 500ms — a 3× latency reduction with no change to the output. asyncio.gather(return_exceptions=True) ensures one failed tool never cancels the others. BackpressureQueue rejects new requests when the in-flight count exceeds MAX_CONCURRENT — protecting the LLM API from overload.

Core classes: AsyncToolOrchestrator, BackpressureQueue, ToolPlan

Lesson 14 — Context Window Management — Compression, Prioritisation & 60% Token Reduction

Four strategies, applied cheapest-first: strip tool call metadata → remove near-duplicates → truncate system prompt to 800 chars → LLM-summarise the oldest half. Most requests exit after strategy 1 or 2. Strategy 4 (the LLM call) triggers only when the others are insufficient. 60% token reduction at 10k requests/day is a meaningful monthly cost difference.

Core class: ContextCompressor

Lessons 15–21

The feedback loop that makes your agent continuously improve without human intervention. By Lesson 21 the agent blocks quality regressions in CI, runs live A/B experiments, heals from failures in milliseconds, improves its own prompt accuracy on a weekly schedule, and uses structured relationship graphs for compliance-queryable audit trails.

Lesson 15 — Evaluation Framework — Automated Quality Gates & CI Integration

EvalCase defines input, must_contain assertions, must_not_contain assertions, and a max_cost_usd cap. EvalHarness runs a suite against a live agent, produces a PASS/FAIL report, and exits with code 1 — blocking the CI merge. A 5 percentage point quality regression is caught in minutes, not days.

Core classes: EvalCase, EvalResult, EvalHarness

Lesson 16 — Vector Store & RAG — Retrieval-Augmented Generation Pipeline

VectorStore indexes documents as dense embeddings and retrieves the top-K most semantically relevant chunks per query. RAGPipeline injects retrieved context into the prompt before the LLM call — grounding responses in real documents rather than parametric memory. Chunk size and overlap are tuned per corpus; wrong values silently destroy retrieval quality.

Core classes: VectorStore, RAGPipeline, ChunkConfig

Lesson 17 — Fine-Tuning Pipeline — LoRA Adapters & Dataset Curation

FineTuningPipeline curates a training dataset from production traces, applies LoRA adapters to reduce GPU memory by 70% vs full fine-tuning, and validates the fine-tuned model against EvalHarness before promotion. A fine-tuned model that regresses on the eval suite never reaches production.

Core classes: TrainingDataset, LoRAConfig, FineTuningPipeline

Lesson 18 — Agent Guardrails — Input Validation, Output Filtering & Policy Enforcement

GuardrailPipeline runs three sequential checks: input policy (topic restrictions, length limits, PII detection), tool call policy (allowed tools per role, parameter bounds), and output policy (factual grounding score, toxicity filter, PII redaction). A request that fails any check is rejected with a structured error — not silently degraded.

Core classes: InputGuardrail, ToolGuardrail, OutputGuardrail, GuardrailPipeline

Lesson 19 — Human-in-the-Loop — Approval Gates, Escalation & Audit Trail

HITLGate pauses execution at configurable decision points and routes the pending action to a human reviewer via webhook. EscalationPolicy defines which tool calls require approval (irreversible writes, high-cost operations, regulated data access). Every approval and rejection is written to the audit log with reviewer identity and timestamp.

Core classes: HITLGate, EscalationPolicy, ReviewRecord

Lesson 20 — Agent Persistence — State Recovery, Checkpointing & Resume

CheckpointManager snapshots full agent state — conversation history, tool results, current step — to durable storage at every state transition. On restart, AgentRecovery replays from the last valid checkpoint rather than from scratch. A checkpoint that is never tested is an assumption, not a guarantee.

Core classes: Checkpoint, CheckpointManager, AgentRecovery

Lesson 21 — Cost Optimisation — Model Routing, Caching & Budget Enforcement

CostRouter classifies each query by complexity and routes it to the cheapest model that can satisfy the quality threshold — local SLM for simple queries, frontier model for complex ones. BudgetEnforcer tracks spend per user, per team, and per agent type in real time and hard-blocks requests that would breach the daily cap.

Core classes: CostRouter, ComplexityClassifier, BudgetEnforcer

Lessons 22–30

Kubernetes, compliance, and five production agents. By Lesson 30 you have a deployment that a security team can audit, a compliance team can certify, and an SRE team can operate — and a portfolio of five agents that demonstrate every technique from Phases 1–3 working together.

Lesson 22 — Streaming Responses — SSE Token Pipeline & Backpressure

StreamingLLM calls the LLM with stream=True and yields tokens as they arrive. The FastAPI route flushes every 5 tokens as an SSE data: frame — single-token flushes are noisy, 20-token flushes feel laggy. X-Accel-Buffering: no prevents nginx from holding the stream. First token under 500ms.

Core class: StreamingLLM

Lesson 23 — Multi-Modal Agent — Vision, Audio & Document Inputs

MultiModalRouter detects input type (text, image, audio, PDF) and dispatches to the correct preprocessing pipeline before the LLM call. VisionTool extracts structured data from images. DocumentParser chunks PDFs with layout awareness — splitting mid-table destroys context. One unified AgentRequest schema regardless of modality.

Core classes: MultiModalRouter, VisionTool, DocumentParser

Lesson 24 — Agent Testing Patterns — Unit, Integration & Load Tests

AgentTestHarness provides deterministic stub LLM responses so unit tests run without an API key and in under 1 second. IntegrationTestSuite spins up the full stack in Docker Compose and validates end-to-end request paths. LoadTestRunner (Locust) measures p95 latency and error rate under 500 concurrent users — the numbers that catch resource leaks before production.

Core classes: AgentTestHarness, IntegrationTestSuite, LoadTestRunner

Lesson 25 — Production Deployment — Docker, Kubernetes & Zero-Downtime Rollouts

maxUnavailable: 0 in the rolling update strategy means zero dropped requests during deploys. runAsNonRoot: true eliminates an entire class of container escape impact. The readiness probe means no traffic arrives until the pod signals ready. The preStop: sleep 5 closes the race condition between SIGTERM and the load balancer’s routing table update. Artifacts: k8s/namespace.yaml, k8s/deployment.yaml, k8s/hpa.yaml, k8s/configmap.yaml

Lesson 26 — Prompt Injection Defense — Detection, Structural Analysis & Runtime Blocking

PromptInjectionDetector runs three layers in under 5ms: regex signatures for known attack patterns, structural analysis for instruction-override attempts, and anomaly scoring against the expected prompt shape. A detected injection is logged with full context and blocked before reaching the LLM — not filtered after.

Core classes: InjectionSignature, StructuralAnalyser, PromptInjectionDetector

Lesson 27 — Agent Versioning — Artifact Tracking, Rollback & Promotion Gates

AgentVersionRegistry stores every deployed agent as an immutable artifact: prompt version, model ID, tool manifest, eval suite hash, and deployment timestamp. PromotionGate runs the eval suite against the candidate version and blocks promotion if pass rate falls below threshold. Rolling back is a single function call — not a manual config edit.

Core classes: AgentVersion, AgentVersionRegistry, PromotionGate

Lesson 28 — Compliance & Audit Log — Immutable Records, Chain-Hash & Evidence Pack

Each ImmutableAuditWriter entry includes a chain_hash — the SHA-256 of the previous entry’s JSON. Modify any entry and the chain breaks. generate_evidence_pack() produces structured output pointing to the actual evidence: S3 bucket ARN, IAM policy document, CloudWatch alert ARNs. Auditors need pointers to real evidence, not prose descriptions.

Core classes: ImmutableAuditWriter · Key function: generate_evidence_pack()

Lesson 29 — Capacity Planning — Load Modelling, Scaling Thresholds & Cost Forecasting

CapacityModel projects required replicas, token budget, and monthly API cost from three inputs: expected RPS, average tokens per request, and target p95 latency. ScalingSimulator replays historical traffic patterns against the model to validate thresholds before they go live. A capacity plan built on assumed traffic numbers fails the first time real traffic arrives.

Core classes: CapacityModel, ScalingSimulator, CostForecast

Lesson 30 — Production Capstone — Five Agents, One Enterprise Platform

All five agents deployed as a single production system. Agent 1 (Secure Customer Service): injection blocked in under 5ms, RBAC enforced, semantic cache active. Agent 2 (Multi-Agent Research): three parallel workers, one failure does not cascade. Agent 3 (Streaming Analytics): first token under 500ms, nginx bypass active. Agent 4 (Self-Healing LLMOps): CircuitBreaker, EvalHarness, and PromptOptimiser running as a closed loop. Agent 5 (Enterprise Deployment): SOC 2 audit trail, HIPAA PHI detection, KEDA queue-depth scaling, Route 53 failover tested and documented. This is the system you put in your portfolio.

Most AI tutorials stop once the model generates a response. Production AI Engineering begins where those tutorials end. Throughout this course, you'll learn how to build AI systems that remain secure under attack, scale under heavy traffic, recover from failures automatically, satisfy enterprise compliance requirements, and continuously improve through automated LLMOps pipelines.

Every lesson focuses on solving a real production engineering challenge using proven architectural patterns—not shortcuts or abstractions. By the final capstone, you won't just have five portfolio-ready AI agents—you'll understand how modern enterprise AI platforms are designed, operated, and trusted in production.

If you’ve made it this far, you’ve already seen that this isn’t another “build an AI chatbot” course. It’s a complete roadmap to engineering secure, scalable, and enterprise-ready AI systems from the ground up.

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