Start with the right foundation.
- Python developers building AI applications
- Data and ML engineers
- Solution architects evaluating agent workflows
- Technical product teams prototyping controlled automation
PRODUCTION AGENT ENGINEERING PATH
Agentic AI with LangChain and LangGraph
Build controlled AI agents with tools, retrieval, state, durable workflows, human approval, evaluation, observability and safety boundaries.
Learn when an agent is appropriate, when a deterministic workflow is safer and how to make either observable. The program focuses on production reasoning: tool contracts, state, checkpoints, retrieval quality, human control, evaluation and failure recovery.
Recommended prerequisites.
- Working Python fundamentals
- Basic API and JSON knowledge
- Introductory understanding of language models and prompting
ROLE-BASED OUTCOMES
Learn to make and defend real implementation decisions.
The goal is not to memorize product menus. Each outcome connects architecture, implementation and operational evidence.
Choose between deterministic workflows, agents and hybrid graphs
Design safe tool interfaces and explicit state transitions
Build retrieval and memory with clear evaluation criteria
Add checkpoints, human approval, traces and recovery to long-running work
Tools and platformsPythonLangChainLangGraphVector retrievalStructured outputsLangSmith conceptsEvaluation datasetsHuman-in-the-loop controls
DETAILED SYLLABUS
One connected path from concepts to production evidence.
Every module includes a practical deliverable so you can explain what you built, why you chose it and how you verified it.
Agent foundations and decision boundaries
Define the job, risk and success criteria before selecting an agent architecture.
- Workflows versus agents
- Tool-calling loops
- Structured outputs
- Autonomy boundaries
Hands-on lab
Classify six automation cases and defend where deterministic code, a workflow or an agent should be used.
Tools and reliable model interfaces
Create narrow tool contracts that validate inputs, handle errors and expose only necessary authority.
- Tool schemas
- Validation
- Idempotency
- Least authority
Hands-on lab
Build read and write tools for a support workflow with validation, confirmation and safe retry behavior.
Retrieval and grounded responses
Treat retrieval as an evaluated system rather than attaching a vector database and hoping.
- Chunking and metadata
- Search and reranking
- Citations
- Retrieval evaluation
Hands-on lab
Create a small policy assistant and measure retrieval failures with a labeled question set.
LangGraph state and control flow
Model nodes, state and conditional edges so execution remains inspectable.
- StateGraph
- Nodes and edges
- Routing
- Subgraphs
Hands-on lab
Implement a triage graph with deterministic routing, specialist nodes and an explicit stop condition.
Durability and human oversight
Use checkpoints and interrupts to pause risky actions and resume after failures.
- Persistence
- Durable execution
- Human-in-the-loop
- Memory scope
Hands-on lab
Add approval before an external write, simulate interruption and prove the workflow resumes correctly.
Evaluation, observability and operations
Measure task success, tool behavior, latency, cost and safety before release.
- Traces
- Offline datasets
- Regression evaluation
- Operational fallbacks
Hands-on lab
Create a release gate with success, citation, tool-error and human-escalation measures.
CAPSTONE WORK
Projects designed for explanation, review and improvement.
A useful portfolio shows decisions and evidence, not screenshots alone.
Controlled support agent
Triage requests, retrieve policy evidence and require approval before account changes.
Evidence to retain- Architecture diagram
- Tool contracts
- Evaluation results
- Escalation runbook
Research workflow
Coordinate search, evidence extraction and cited synthesis with explicit quality checks.
Evidence to retain- Graph definition
- Source-quality rules
- Failure tests
- Trace review
Agent production readiness review
Assess an agent prototype for authority, privacy, reliability and operations risk.
Evidence to retain- Threat model
- Evaluation plan
- Observability map
- Go-live checklist
CONTINUE WITH PRIMARY SOURCES
Use current official documentation alongside guided practice.
Technology changes. These primary references help you verify current features and continue learning after the course. ITCertPath is an independent training brand and is not endorsed by the listed vendors.
COURSE QUESTIONS
What to confirm before you enrol.
Ask for a written syllabus, delivery plan, trainer profile, lab arrangement, fee breakdown and cancellation terms.
Do I need LangGraph for every AI agent?
No. LangChain agents can be suitable for straightforward tool-calling applications, while LangGraph is useful when you need explicit state, branching, persistence, interrupts or low-level orchestration control.
Does the course teach multi-agent systems?
It introduces subgraphs and specialist delegation, but only after tool, state and evaluation foundations. Multiple agents add coordination and observability costs, so the course does not treat them as the default architecture.
Which model provider is required?
The concepts are designed to be model-provider aware but not locked to one model. Confirm supported providers, API costs and the current lab setup before enrolling. API usage may incur separate provider charges.
PLAN YOUR NEXT SKILL
Discuss Agentic AI training with an advisor.
Tell us your experience, target role, time zone and preferred learning format. We will share the current options without promising a batch that has not been confirmed.