Learn to design, build and evaluate reliable AI agents with Python, LangChain, LangGraph, retrieval, tool calling, human approval and production guardrails.
This practical online Agentic AI course moves from a single controlled tool call to stateful, observable agent workflows. You will learn when an agent is appropriate, when deterministic code is safer, and how to test authority, retrieval quality, cost, latency and failure recovery before release.
The goal is not to memorize product menus. Each outcome connects architecture, implementation and operational evidence.
Choose between deterministic workflows, single agents and graph-based orchestration
Design narrow tool interfaces with validation, least authority and safe retries
Build retrieval and memory with measurable grounding criteria
Model state, routing, checkpoints and human approval in LangGraph
Trace model and tool behavior and create regression evaluations
Present a production-readiness review covering security, cost, latency and recovery
Good agent engineering starts with the decision boundary—not with adding more agents. The course compares three patterns and makes their trade-offs visible.
Code owns the sequence. Use it when steps and validation rules are known and predictability matters most.
Example: extract → validate → approve → writeThe model chooses among narrow tools inside explicit limits. Use it when requests vary but one agent can retain responsibility.
Example: support triage with read-only toolsState, routing and checkpoints are explicit. Use it when work must pause, resume, branch or reach a bounded specialist.
Example: research with evidence reviewEvery module includes a practical deliverable so you can explain what you built, why you chose it and how you verified it.
Define the job, risk and success criteria before selecting an agent architecture.
Classify six automation cases and defend where deterministic code, a workflow or an agent should be used.
Create narrow tool contracts that validate inputs, handle errors and expose only necessary authority.
Build read and write tools for a support workflow with validation, confirmation and safe retry behavior.
Treat retrieval as an evaluated system rather than attaching a vector database and hoping.
Create a small policy assistant and measure retrieval failures with a labeled question set.
Control how a model selects tools, delegates bounded work and decides when to stop.
Implement a request router and compare a deterministic branch with a model-selected specialist.
Model nodes, state and conditional edges so execution remains inspectable.
Implement a triage graph with deterministic routing, specialist nodes and an explicit stop condition.
Use checkpoints and interrupts to pause risky actions and resume after failures.
Add approval before an external write, simulate interruption and prove the workflow resumes correctly.
Use delegation and shared tool protocols only when they reduce complexity or improve ownership.
Design a two-specialist workflow and document where authorization and context must be checked.
Treat prompts, retrieved content and tool results as untrusted inputs and limit every side effect.
Threat-model an agent, add approval to a sensitive action and test malicious retrieved instructions.
Measure task success, tool behavior, latency, cost and safety before release.
Create a release gate with success, citation, tool-error and human-escalation measures.
Package the workflow with configuration, monitoring, budgets and a documented rollback path.
Run a production-readiness review and demonstrate a graceful fallback when a model or tool is unavailable.
Each failure is turned into a testable engineering control, not a vague prompt improvement.
Restrict permissions, validate arguments and require approval before sensitive writes.
Treat user and retrieved text as untrusted; separate instructions from evidence and test adversarial inputs.
Measure retrieval separately, retain citations and provide a clear insufficient-evidence response.
Set stop conditions, turn budgets, timeouts and model-specific cost thresholds.
Return structured errors, use idempotent retries and expose failures in traces.
Maintain evaluation datasets and block releases when task, safety or citation measures regress.
A useful portfolio shows decisions and evidence, not screenshots alone.
Triage requests, retrieve policy evidence and require approval before account changes.
Evidence to retainCoordinate search, evidence extraction and cited synthesis with explicit quality checks.
Evidence to retainAssess an agent prototype for authority, privacy, reliability and operations risk.
Evidence to retainTechnology 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.
Ask for a written syllabus, delivery plan, trainer profile, lab arrangement, fee breakdown and cancellation terms.
The learning path covers agent architecture, Python tool calling, structured outputs, RAG, LangChain, LangGraph state and persistence, human approval, multi-agent design choices, MCP concepts, security, evaluation, tracing and production readiness.
It is suitable for learners new to agent engineering who already have working Python and basic API knowledge. Complete Python fundamentals first if functions, packages, JSON and HTTP requests are unfamiliar.
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.
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.
Yes. RAG and tool calling are implemented and evaluated in the learning path. MCP is taught as an integration concept, including tool discovery, trust boundaries and authorization decisions; confirm the current hands-on MCP lab before enrolling.
This is a practical skills-training program from ITCertPath, not an official certification from OpenAI, LangChain or another model provider. Ask for the current completion-certificate terms before enrolling.
ITCertPath can discuss an online delivery option for learners in Hyderabad, across India and other time zones. Trainer availability, schedule, language, fees and live-session format must be confirmed in writing for the current cohort.
They are not fixed on this page because schedules and delivery arrangements can change. Request the current written course plan for confirmed duration, fees, trainer, time zone, lab access and cancellation terms.
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.
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.