What You'll Learn
Prerequisites
- No prior programming experience needed
- Basic computer knowledge
- A laptop with internet access
- Enthusiasm to learn!
Tools & Setup
📖 Tutorial Chapters & Curriculum
Follow the structured learning path from beginner to advanced
1
Autonomous Decision Systems: From Suggestions to Decisions
Learn how agents choose actions under uncertainty using policies, rewards, and constraints.
2
Agent Frameworks Overview: What They Solve (and What They Don’t)
Compare popular agent frameworks and understand the real problems they address.
3
RAG for Agents: Why Retrieval Changes Behavior
Learn how retrieval augments agent decisions and reduces hallucinations.
4
Agent Evaluation: What to Measure (Beyond ‘Seems Good’)
Define measurable quality for agents: success rate, cost, latency, and safety.
5
Deploying Agents: Reference Architecture for Production
Learn the standard production architecture: gateway, orchestrator, tools, memory, and observability.
6
Capstone Overview: How to Build an Agentic System Like a Product
Capstone guidance: define scope, choose tools, design memory, and set evaluation targets.
7
The Planning Problem in Agentic AI: Goals, Tasks, and Constraints
Why planning is the backbone of autonomous agents—and why it fails in production.
8
Memory in Agents: The Missing Layer Between Chatbots and Autonomy
Learn why memory is essential, what types exist, and how to design them safely.
9
Tool-Using Agents: From Chat to Action (Safely)
Learn how agents call tools, why schemas matter, and how to keep actions safe in production.
10
Multi-Agent Systems: When One Agent Isn’t Enough
Understand when multi-agent design helps—and when it’s unnecessary complexity.
11
Decision Under Uncertainty: Confidence, Risk, and Verification
Use confidence thresholds and verification steps to reduce risky autonomous actions.
12
LangGraph Fundamentals: Modeling Agents as State Machines
Design reliable agents using explicit states, transitions, and guard conditions.
13
Indexing Strategy: Chunking, Metadata, and Refresh Cycles
Chunk properly, add metadata filters, and keep your index fresh.
14
Test Sets for Agents: Scenarios, Edge Cases, and Regression Suites
Build a realistic test suite with scenarios and edge cases that mirror production traffic.
15
Latency Engineering: Fast Agents Without Bad Answers
Reduce latency using caching, parallel tools, smaller models, and early stopping rules.
16
Project 1: Autonomous Research Assistant (RAG + Tool Use)
Build a research agent that gathers sources, summarizes evidence, and produces a structured brief.
17
ReAct: Reason + Act Loops That Actually Work
Build robust ReAct-style agents with tool calls and observation-driven reasoning.
18
Working Memory: Context Windows, Scratchpads, and State
Use short-term memory effectively without blowing up tokens or leaking private data.
19
Function Calling & JSON Schemas That Don’t Break
Build tool schemas that are strict enough for safety but flexible enough for real-world inputs.
20
Roles, Specialization, and Agent Teams (Manager–Worker Pattern)
Design teams: a manager plans and delegates, workers execute specialized tasks.
21
Reward Models and Utility Functions (Without the Math Pain)
Define what the agent optimizes: speed vs quality vs cost—and make it explicit.
22
CrewAI Style Teams: Roles, Tasks, and Collaborative Runs
Implement agent teams with defined roles, task assignment, and result aggregation.
23
Retrieval Tuning: Top-K, Filters, Re-Ranking, and Thresholds
Make retrieval precise with thresholds, re-rankers, and domain filters.
24
Safety Guardrails: Content Policies, Red Teams, and Refusal Design
Design refusals, red team your system, and enforce policies at tool boundaries.
25
Cost Controls: Token Budgets, Tool Budgets, and Model Routing
Keep spending predictable by routing tasks to appropriate models and enforcing budgets.
26
Project 2: Customer Support Copilot (Triage + Draft + Escalation)
Design a support agent that routes tickets, drafts answers, and escalates sensitive issues.
27
Tree-of-Thoughts: Exploring Multiple Reasoning Paths
When one chain-of-thought is not enough—search over thoughts, not tokens.
28
Long-Term Memory Patterns: Profiles, Preferences, and Facts
Design durable memory that stays correct over time and doesn’t become a liability.
29
API Orchestration Patterns: Fan-out, Fan-in, and Pipelines
Orchestrate multiple APIs reliably: parallel calls, aggregation, and step-by-step pipelines.
30
Communication Protocols: Messages, Shared State, and Contracts
Prevent chaos with structured messages, shared state, and clear contracts between agents.
31
Policy Networks vs Rule Engines: Choosing the Right Brain
When should decisions be rules, and when should they be model-driven policies?
32
AutoGen Concepts: Agent Conversations with Clear Termination
Use agent-to-agent conversation patterns with termination rules and safety constraints.
33
Agentic RAG: Retrieval as a Tool in a Planning Loop
Use retrieval iteratively: retrieve → reason → retrieve again, until evidence is sufficient.
34
Prompt Injection Defense: How Agents Get Tricked
Stop malicious instructions hidden in docs, webpages, or user inputs from hijacking your agent.
35
Scaling Infrastructure: Queues, Workers, and Async Agent Runs
Scale agent workloads using queues, worker pools, and asynchronous workflows.
36
Project 3: Autonomous DevOps Assistant (Runbooks + Safe Actions)
Build an agent that follows runbooks, checks metrics, and proposes safe remediation steps.
37
Reflexion: Self-Critique, Feedback, and Iterative Improvement
Add reflection loops so agents learn from mistakes without retraining.
38
Vector Memory with Embeddings: Retrieval That Feels Like Recall
Build semantic memory with embeddings and tune retrieval for real user queries.
39
Retries, Timeouts, and Idempotency for Agent Actions
Prevent double-charges and duplicated writes with idempotency keys, safe retries, and timeouts.
40
Coordination Strategies: Parallelism, Voting, and Debate
Get better answers using parallel workers, majority voting, or structured debate.
41
Goal Management: Priorities, Deadlines, and Trade-offs
Handle multiple goals with priority queues, deadlines, and conflict resolution.
42
Semantic Kernel: Skills, Planners, and Enterprise Integrations
Build enterprise-friendly agents using skills, planners, and connectors.
43
Grounded Generation: Citing Sources and Preventing Hallucinations
Make agents quote evidence, cite doc IDs, and refuse when evidence is missing.
44
Hallucination Control: Grounding, Verification, and Uncertainty
Reduce hallucinations with retrieval, tool verification, and uncertainty-aware responses.
45
Secrets and Key Management for Tooling and LLM Providers
Protect API keys and secrets with vaults, rotation, and least privilege access.
46
Project 4: Multi-Agent Content Pipeline (Writer + Editor + SEO Reviewer)
Create a multi-agent workflow that produces content with checks for quality and SEO.
47
Plan-and-Execute Architecture: Separation of Strategy and Action
Design an agent that plans once, executes safely, and replans only when needed.
48
Episodic Memory: Storing Experiences and Learning from Them
Capture episodes, outcomes, and lessons so agents improve across runs.
49
Permissions & Policy Enforcement for Tool Calls
Implement authorization, scopes, and policy checks so agents can’t exceed user permissions.
50
Conflict Resolution: Handling Disagreements Between Agents
Resolve conflicting outputs using evidence, confidence, and constraint checks.
51
Decision Logging and Accountability (Why Audits Matter)
Log decisions so you can debug incidents and meet compliance requirements.
52
Framework Selection Guide: Pick the Simplest Thing That Works
How to choose a framework based on scale, observability, and failure tolerance.
53
Security in RAG: Access Control, Tenant Isolation, and Redaction
Prevent data leakage with ACLs, tenant filters, and redaction at retrieval time.
54
Sandboxing and Safe Execution for Code-Running Agents
Run code safely with containers, resource limits, and output validation.
55
Monitoring & Alerting: Detecting Cost Spikes and Failure Storms
Set alerts for cost, latency, tool failures, and abnormal behavior patterns.
56
Project 5: Agentic Analytics Assistant (Queries + Explanations + Dashboards)
Build an assistant that generates SQL, validates results, and explains insights clearly.
57
Hierarchical Task Decomposition: From Intent to Subtasks
Turn big, vague goals into manageable subtasks with clear stopping criteria.
58
Memory Quality: Relevance, Recency, and Truthfulness
Stop your agent from retrieving the wrong memory at the worst time.
59
Tool Output Summarization: Keep Context Small, Keep Decisions Sharp
Learn how to compress noisy tool results into clean observations the agent can reason over.
60
Multi-Agent Failure Modes: Collusion, Loops, and Amplified Errors
Learn the nasty edge cases: agents reinforcing each other’s mistakes and looping forever.
61
Tooling for Multi-Agent: Shared Memory, Traces, and Runbooks
Production requirements: shared memory services, tracing, incident runbooks, and replayability.
62
Bandits and Exploration: Let Agents Learn Safer Choices Over Time
Use safe exploration (A/B tests, bandits) to improve decisions without breaking users.
63
Production Setup: Tracing, Evaluation, and Cost Controls
Wire in tracing, eval harnesses, and token budgets so agents don’t explode in production.
64
RAG Evaluation: Measuring Answer Grounding and Retrieval Quality
Evaluate retrieval precision/recall and whether the final answer used the right evidence.
65
Observability for Agents: Traces, Spans, and Failure Debugging
Instrument every run so you can reproduce failures and fix them fast.
66
Versioning Prompts and Tools: Safe Rollouts with Feature Flags
Roll out changes safely with prompt versions, tool versions, and staged deployments.
67
Capstone Evaluation: Scoring Rubric and Demo Checklist
A practical rubric: reliability, safety, UX, observability, and cost control.
68
Search, Heuristics, and Best-First Planning for LLM Agents
Practical planning algorithms you can combine with LLM reasoning.
69
Memory Write Policies: What to Save, When to Save, and Consent
Avoid memory creep, privacy issues, and bloated stores with strict write rules.
70
Human-in-the-Loop: Confirmations and Approval Flows
Add confirmation steps so autonomy doesn’t become accidental automation.
71
Design Exercise: Build a Research Team of Agents
A hands-on blueprint for creating a multi-agent research workflow with roles and quality gates.
72
Decision Systems Case Study: Autonomous Customer Support Triage
Design an agent that classifies tickets, routes them, and drafts responses with safety gates.
73
Migration Pattern: From DIY Agent to Framework-Based Agent
A step-by-step approach to migrate without breaking production systems.
74
Case Study: Build a Policy-Aware Support Agent with RAG
Blueprint for a support agent that answers only from policy docs and escalates when uncertain.
75
Safety Case Study: Building a Finance Assistant with Strict Guardrails
A practical blueprint for a finance assistant that verifies data and refuses risky requests.
76
Deployment Case Study: Shipping a Support Agent End-to-End
A full deployment blueprint: tools, memory, RAG, monitoring, and safe rollouts.
77
Going Beyond: Hardening Your Agent for Real Users
Final hardening steps: red teaming, load testing, onboarding UX, and incident playbooks.
78
Tool Selection Policies: When to Call Tools vs Think
Reduce cost and hallucinations by teaching the agent when NOT to call tools.
79
RAG vs Memory: Where Knowledge Should Live
Separate factual knowledge bases from personalized memory to keep systems stable.
80
Building a Minimal Tooling Layer: Tool Registry, Router, and Tracing
How to implement a clean tool registry and tracing layer for production agents.
81
Failure Modes in Planning: Loops, Drift, and Over-Planning
Detect and fix common agent planning failures before your users do.
82
Observability & Debugging Memory: Logs, Traces, and Evaluation
How to debug ‘the agent remembered wrong’ with practical tooling and metrics.
🎯 Interview Preparation
Top Python interview questions organized by difficulty
Easy
30 Questions
Freshers / Entry Level
- What is Agentic AI and how does it differ from traditional AI systems? (Advanced Perspective 1)
- Explain the Observe–Think–Act loop in autonomous systems. (Advanced Perspective 2)
- What are the key components of an autonomous AI agent? (Advanced Perspective 3)
- How do LLMs function as cognitive engines in agentic systems? (Advanced Perspective 4)
- What is ReAct architecture? (Advanced Perspective 5)
Medium
50 Questions
Experienced / Mid-Level
- What is Agentic AI and how does it differ from traditional AI systems? (Advanced Perspective 31)
- Explain the Observe–Think–Act loop in autonomous systems. (Advanced Perspective 32)
- What are the key components of an autonomous AI agent? (Advanced Perspective 33)
- How do LLMs function as cognitive engines in agentic systems? (Advanced Perspective 34)
- What is ReAct architecture? (Advanced Perspective 35)
Hard
30 Questions
Senior / Lead Level
- What is Agentic AI and how does it differ from traditional AI systems? (Advanced Perspective 81)
- Explain the Observe–Think–Act loop in autonomous systems. (Advanced Perspective 82)
- What are the key components of an autonomous AI agent? (Advanced Perspective 83)
- How do LLMs function as cognitive engines in agentic systems? (Advanced Perspective 84)
- What is ReAct architecture? (Advanced Perspective 85)
Nagmani Solanki
Digital Marketing
Edugators platform is the best place to learn live classes, and live projects by which you can understand easily and have excellent customer service.
Saurabh Arya
Full Stack Developer
It was a very good experience. Edugators and the instructor worked with us through the whole process to ensure we received the best training solution for our needs.
Praveen Madhukar
Web Design
I would definitely recommend taking courses from Edugators. The instructors are very knowledgeable, receptive to questions and willing to go out of the way to help you.
Need To Train Your Corporate Team ?
Customized Corporate Training Programs and Developing Skills For Project Success.
Get Newsletter
Subscibe to our newsletter and we will notify you about the newest updates on Edugators
