SV Infotech · Dilsukhnagar, Hyderabad
AI Agents, LangGraph & MCP
Personal study sheet
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Set your starting point
Useful foundations: Python, JSON, APIs and familiarity with an LLM application provide a useful starting point.
Explore the learning roadmap
01Define a bounded agent task
Decide what the system may decide, which operations require review and when it must stop. Compare an agent loop with a simpler deterministic workflow.
02Model state and transitions
Describe the information carried between steps and the conditions that choose the next action. Consider interrupted runs and repeated work when designing a stateful workflow.
03Design tool contracts
Specify arguments, responses, permissions and failure behaviour. Validate tool inputs independently of the model and make consequential actions reviewable.
04Understand MCP boundaries
Discuss clients, servers and exposed capabilities. Treat tool descriptions and retrieved content as data that can be wrong or untrusted rather than as authority to expand permissions.
05Evaluate task completion
Test successful, rejected and interrupted paths with a clear expected outcome. Record tool calls, failures, latency and cost without logging unnecessary personal information.
06Plan human review and recovery
Provide a useful handoff when the agent is uncertain or a tool fails. Ask which orchestration libraries, protocol features and practical exercises a confirmed programme includes.
AI Agents, LangGraph & MCP: a worked study example
Illustrative scenario for discussion and self-study.
Diagram a fictional research assistant that can retrieve documents and draft an email but cannot send it without approval. Specify what happens when retrieval fails or the user changes the task. A useful design makes the stopping rule, persisted state and permitted tool actions explicit.
What to check in your work
- Reject a tool request outside the declared input or permission boundary.
- Explain whether a retry repeats a read or a consequential action.
How this pathway compares
Agent orchestration and MCP solve related but different integration problems. Generative AI provides the grounding and evaluation context; AI Automation explores how a workflow can be dependable even when some steps use a model.
Check your reasoning
What should happen if an agent retries a tool that changes a record?
Suggested explanation
The tool contract should make repeated requests and their effects understandable. Use an appropriate duplicate-prevention or idempotency mechanism and require review where the action warrants it. A language model's intention is not a reliable substitute for server-side validation and permissions.
Plan the conversation
Ask about the actual syllabus, practical environment and feedback arrangements. Confirm fees, duration, trainer and current batch schedule directly; this sheet supplies none of those details.
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