Mastering Agentic AI Engineering with Python: From Foundation to Production

Define agentic AI, core concepts, and enterprise use-case taxonomy
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Map high-level agent architectures and component responsibilities
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Model the agent decision loop: perception, reasoning, action, feedback
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Specify agent goals, constraints, and measurable success criteria
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Establish governance, ethics, and human-in-the-loop oversight patterns
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Agentic AI Ecosystem
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Agent Types
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Current Industry Trends
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Select and benchmark LLMs for agent workloads
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Prompt Engineering: Zero-shot, Few-shot, Chain of Thought, Self-consistency, Reflection prompting
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Modern Reasoning Techniques: ReAct, Tree of Thoughts, Graph of Thoughts, Reflection, Self-Refine
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Design system and few-shot prompts to shape agent behavior
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Chain of Thought implementation for designing agent workflow
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Engineer dynamic prompting and context-window strategies
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Fine-tune LLMs with LoRA and PEFT for agent tasks
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Apply instruction-tuning and alignment methods for safety
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Optimize inference with quantization, batching, and streaming
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Control outputs via temperature, sampling, and biasing
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Craft chain-of-thought prompts for reliable multi-step reasoning
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Structure LLM outputs using JSON schemas and validators
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Detect and mitigate hallucinations and toxic or unsafe outputs
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Design a production-ready Python agent project structure
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Implement an asynchronous agent runtime loop with asyncio
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Build a modular action dispatcher and task queue system
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Create a pluggable tool adapter and registry interface
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Implement a prompt-and-response processing pipeline
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Build an action scheduler with priorities, delays, and retries
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Implement lightweight session state tracking and context management
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Write unit and integration tests for core agent modules
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Add structured logging and tracing hooks for observability
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Profile and optimize agent runtime performance in Python
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Package the agent as a reusable Python library and CLI
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Simulate environments and execute end-to-end local scenarios
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Hands on Lab: Build a fully functioning agent without frameworks
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Define precise function schemas for LLM-driven function calls
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Implement modular tool adapters and API wrappers in Python
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Validate, sanitize, and normalize tool inputs and outputs
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Implement synchronous and asynchronous tool invocation patterns
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Design retries, timeouts, and circuit-breakers for tool calls
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Enforce tool sandboxing and capability-based permissions
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Instrument tool calls with logging, traces, and structured events
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Mock tools and write unit and integration tests for function calling
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Implement dynamic tool discovery, registration, and versioning
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Build graceful fallback, error parsing, and automated recovery strategies
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Hands on Lab: Build a tool-enabled autonomous assistant
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Define and Map Memory Types to Agent Goals
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Types of Memory: Short-Term, Long-Term, Episodic Memory, Semantic Memory,
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Design Memory Architecture and Interfaces
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Select Storage and Indexing Strategies for Memory
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Implement Memory Lifecycle, Retention, and Pruning
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Build Working Memory and Context Buffers
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Design Episodic-to-Semantic Memory Consolidation
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Apply Compression and Summarization for Memory Scaling
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Implement Memory Versioning and Consistency Controls
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Secure Memory: Encryption, Access Controls, Compliance
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Instrument and Debug Memory with Analytics and Traces
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Define and Measure Memory Quality and Retrieval Metrics
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Integrate Memory with Reasoning, Tools, and Context Windows
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Hands on Lab: Build persistent memory architecture
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Architect a production-grade RAG system for agents
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Prepare and chunk corpora for high-quality retrieval
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Build an end-to-end embedding pipeline and validate models
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Select, deploy, and benchmark vector databases for scale
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Implement hybrid dense+BM25 retrieval and tuning
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Integrate retrieval into agent reasoning and tool flows
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Assemble context and stitch prompts to minimize hallucination
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Implement incremental indexing and real-time data refresh
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Optimize retrieval latency with caching, sharding, and batching
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Add reranking, relevance scoring, and user feedback loops
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Record provenance, attribution, and compliance metadata for retrieved content
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Design RAG-specific tests, evaluations, and A/B experiments
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Hands on Lab: Develop Enterprise-grade RAG implementation step by step
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Design enterprise ontologies and schema governance for agent knowledge
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Build robust entity linking, resolution, and canonicalization pipelines
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Fuse graph topology with embedding spaces for hybrid knowledge modeling
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Model temporal and event-based facts for time-aware agent reasoning
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Apply graph neural networks for multi-hop inference and relation prediction
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Implement provenance, attribution, and explainable KG traces for agents
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Build continual KG ingestion, deduplication, and conflict-resolution flows
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Optimize graph storage, indexing, and caching for sub-second agent queries
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Query and traverse knowledge graphs with SPARQL and property graph APIs
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Orchestrate human-in-the-loop KG curation, annotation, and active learning
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Hands on Lab: Develop a Graph-powered memory system.
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Assess Planning Approaches and Select a Planner
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Model Goals, Metrics, and Constraints for Planning
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Encode Domains and Problems in PDDL for Planners
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Implement Heuristic Search (A*, IDA*) for Plan Synthesis
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Build Hierarchical Task Networks (HTN) for Complex Tasks
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Implement Temporal and Resource-Constrained Planning
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Integrate Probabilistic Planning and MCTS for Uncertainty
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Design Replanning and Failure Recovery Strategies
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Implement Plan Execution, Monitoring, and Progress Tracking
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Design Internal Planner-Executor Contracts and Control Semantics
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Simulate, Validate, and Stress-Test Plans with Scenarios
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Optimize Plans with Domain-Specific Heuristics and Cost Models
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Create Explainable Plans and Developer Debugging Views
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Hands on Lab practice with coding: Build a planning engine from scratch
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Design end-to-end autonomous workflow blueprints
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Model workflow states, transitions, and lifecycle
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Implement event-driven triggers, webhooks, and schedulers
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Orchestrate parallelism and conditional task execution
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Integrate external services and connectors for workflows
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Implement durable checkpointing and task replay
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Build retry, backoff, and compensating transactions
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Design human-in-the-loop approvals and escalation paths
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Test workflows with deterministic simulations and mocks
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Version, deploy, and roll back workflow definitions
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Create run-level audit trails and data lineage for workflows
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Optimize workflow cost, latency, and resource utilization
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Hands on Lab: Build autonomous business workflow agent.
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Define Agent Roles, Responsibilities, and Interaction Boundaries
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Design Interaction Topologies: Hierarchical, Peer-to-Peer, and Brokered
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Architect Coordination Strategies: Centralized, Decentralized, Hybrid
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Partition Agents for Scale: Sharding, Clustering, and Load Balancing
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Implement Task Allocation Patterns: Contract Nets and Market-Based Flows
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Design Resilience: Redundancy, Failover, and Graceful Degradation
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Specify State Distribution and Consistency Models Across Agents
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Create Modular Interfaces and Service Boundaries for Agent Components
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Establish Governance, Access Control, and Safety Policies for Architectures
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Design Auditability and Provenance Architecture for Multi-Agent Actions
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Build Simulation and Validation Harnesses for Emergent Behavior Testing
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Optimize Latency, Throughput, and Cost Tradeoffs in Architecture Topologies
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Hands on Lab: Develop a multi-agent collaboration system
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Specify agent communication requirements and interaction patterns
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Design interoperable message schemas and lightweight ontologies
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Implement REST and WebSocket messaging for real-time agents
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Integrate message brokers for asynchronous agent messaging
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Build gRPC and Protobuf RPCs for low-latency agent calls
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Implement conversation management and turn-taking protocols
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Implement negotiation and contract-net style protocols
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Ensure reliable messaging: idempotency, ordering, and retries
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Test and simulate agent communication behaviors at scale
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Hands on Lab: Develop a complete agent communication platform step by step
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Map MCP core components and responsibility boundaries
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Design MCP internal event bus and dataflow patterns
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Implement MCP plugin and extension lifecycle APIs
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Orchestrate model and tool chains inside the MCP
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Manage MCP state, transactions, and consistency models
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Implement MCP scheduling, queuing, and prioritization
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Apply fault tolerance and graceful degradation strategies in MCP
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Build MCP observability: distributed tracing, metrics, and logs
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Test MCP components with unit, integration, and chaos tests
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Embed compliance, audit trails, and change governance in MCP
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Optimize MCP performance: latency, throughput, and resource use
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Automate MCP versioning, rollbacks, and migration workflows
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Integrate external services, SDKs, and adapters with MCP
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Debug and profile live MCP workflows and bottlenecks
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Hands on Lab: Build MCP Server from scratch
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Hands on Lab 2: Develop Filesystem MCP, Database MCP, CRM MCP, ERP MCP, Internal Tools MCP
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Plan enterprise deployment strategy and requirements for MCP solutions
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Package MCP components as Helm charts and Kubernetes operators
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Automate CI/CD pipelines for MCP builds, tests, and releases
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Implement canary, blue‑green, and rolling deployment strategies
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Configure multi‑tenant isolation, namespaces, and resource quotas
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Integrate enterprise identity, audit logging, and governance controls
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Design backup, disaster recovery, and data migration procedures
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Create deployment testing: smoke, integration, and synthetic checks
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Build deployment observability pipelines and alerting playbooks
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Prepare operational runbooks, rollback plans, and SRE playbooks
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Hands on Lab: Deploy MCP server to Microsoft Azure
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Install and Configure LangChain and LangGraph SDKs
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Create Custom LangChain Chains and Modular Components
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Develop LangGraph Adapters for LangChain Agents
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Map Graph Schemas to Agent Data Models
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Turn LangGraph Nodes into Retrieval and Vector Indexes
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Implement Graph Synchronization and Update Pipelines
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Build Schema-driven Prompt Templates from LangGraph
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Use LangChain Callbacks and LangGraph Events for Tracing
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Unit Test and Simulate Tools with LangChain Test Harnesses
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Compose Hybrid Chains Combining Graph Queries and LLM Calls
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Profile and Tune LangChain Agents for Low Latency
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Package and Distribute LangChain Agents as Reusable Modules
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Integrate Third-party Data Connectors into LangGraph Pipelines
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Implement Governance Hooks and Metadata for LangGraph-driven Agents
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Hands on Lab: Develop an Enterprise LangGraph Agent
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Survey and select agent frameworks by target use case
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Compare framework architectures and runtime patterns
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Analyze framework API models and adapter strategies
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Port a core Python agent to an alternative framework (lab)
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Build a cross-framework tool adapter for function calls
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Prototype cross-framework state sync and memory bridges
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Benchmark latency, throughput, and cost across frameworks
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Audit framework security, privacy, and compliance controls
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Implement observability and unified debugging across frameworks
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Scale agents: orchestration and deployment patterns per framework
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Extend frameworks with custom plugins and connectors (lab)
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Decide and prototype a hybrid multi-framework architecture
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Differe Agent Framworks: CrewAI, AutoGen, Semantic Kernel, PydanticAI, OpenAI Agents SDK, Haystack Agents, SmolAgents
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Agent Framework Comparison with metrics: Performance, Cost, Flexibility, Production readiness
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Hands on Lab: Implement same use case using multiple frameworks.
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Design OpenAPI Contracts for Agent Capabilities
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Generate SDKs and Client Libraries from API Specifications
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Implement REST and gRPC Endpoints for Agent Actions
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Handle Long-Running Agent Tasks with Queues and Webhooks
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Stream Agent Responses with WebSockets and Server-Sent Events
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Agent Service Layer and API Gateway
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Design Idempotent, Versioned, and Backward-Compatible APIs
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Implement Backend Orchestration and Task Routing Patterns
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Model Agent Data: Schemas, Persistence, and Transaction Patterns
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Scale Agent Backends with Caching, Throttling, and Load Strategies
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Test APIs: Contract, Integration, and Mocked LLM End-to-End Tests
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Hands on Lab: Build agent backend service
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Set up cloud projects, billing, and service quotas for agent deployments
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Containerize Python agents with reproducible Docker images
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Create and manage container registries and artifact lifecycles
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Define infrastructure as code with Terraform for agent environments
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Deploy agents to Kubernetes clusters using Helm and Kustomize
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Run agents serverlessly on Cloud Run, AWS Lambda, and Azure Functions
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Provision GPUs/TPUs and configure accelerated inference runtimes
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Configure VPCs, subnets, and network policies for agent isolation
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Integrate message brokers and pub/sub for event-driven agents
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Implement autoscaling and cluster resource management for agents
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Manage persistent volumes and object storage for agent data
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Implement blue-green and canary release strategies on cloud
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Build CI/CD pipelines for agent releases with GitHub Actions
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Deploy multi-region and failover architectures for high resilience
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Hand on Lab: Deploy production agent system to Azure.
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Conduct agent threat modeling and risk assessment
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Implement authentication, authorization, and RBAC for agents
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Secure secrets and credential management for agents
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Defend against prompt injection and adversarial inputs
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Apply secure coding and dependency vulnerability management
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Harden agent runtime with sandboxing and least-privilege execution
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Secure agent communications: encryption, TLS, and mutual authentication
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Build secure CI/CD and supply chain protections for agents
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Implement audit logging, forensics, and tamper-evident trails
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Plan incident response, red teaming, and continuous security testing
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Zero Trust Architecture
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AI Governance
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Hands on Lab: Lab Secure an enterprise agent
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Define KPIs and success metrics for agent behaviors
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Implement structured logging, tracing, and telemetry
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Build real-time observability dashboards and alerts
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Measure and profile agent latency, throughput, and reliability
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Detect concept, data, and performance drift automatically
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Automate regression, integration, and smoke testing for agents
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Conduct human-in-the-loop evaluation and feedback workflows
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Design and run controlled A/B and canary experiments
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Optimize compute, memory, and operational cost for agents
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Create continuous evaluation pipelines and retraining triggers
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Logging Tools: LangSmith, LangFuse, OpenTelemetry, Grafana, Prometheus
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Hands on Lab: Full observability stack
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Define capstone project requirements and measurable success criteria
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Map enterprise stakeholder roles, responsibilities, and governance model
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Consolidate architecture decisions and create an integration dependency map
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Plan CI/CD pipeline strategy and phased release schedule
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Implement automated testing and validation pipelines for agent components
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Prepare and execute user acceptance testing (UAT) plans
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Establish data governance, privacy, and regulatory compliance checklist
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Create operational runbooks and on‑call incident playbooks
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Package, version, and distribute agent artifacts for enterprise delivery
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Conduct performance and scalability acceptance rehearsals
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Prepare operator training materials and run hands‑on workshops
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Execute model and data provenance documentation and audit trails
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Plan rollout, migration, and enterprise change management activities
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Coordinate legal, licensing, and procurement handoff procedures
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Run final integration dry‑run and go/no‑go decision checklist
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Define post‑launch support SLAs, KPIs, and escalation paths
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Assemble demo environment and conduct stakeholder acceptance demo
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Consolidate deliverables, produce handover documentation, and obtain sign‑off
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Hands on Lab Project: Deploy complete enterprise-grade system to Azure.
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Mastering Agentic AI Engineering with Python: From Foundation to Production

Course Curriculum

Instructor