Agentic AI & Multi-Agent Systems
Build autonomous AI agents using AutoGPT, CrewAI, and multi-agent frameworks. Master orchestration and real-world automation.
Autonomous Agents
Build self-directed AI systems with AutoGPT & CrewAI.
Multi-Agent Orchestration
Coordinate complex agent workflows and interactions.
Real-World Automation
Deploy agents for business process automation.
Agentic AI & Multi-Agent Systems
Master the cutting-edge field of autonomous AI agents. Build intelligent systems that can reason, plan, and execute complex tasks independently using AutoGPT, CrewAI, and advanced agent frameworks.
Why Agentic AI?
Agentic AI represents the next frontier in artificial intelligence—systems that can autonomously break down complex goals, make decisions, use tools, and collaborate with other agents to accomplish sophisticated tasks. Unlike traditional AI that follows fixed patterns, AI agents can adapt, learn, and improve their strategies over time.
This advanced program teaches you to design and build autonomous agent systems that can handle real-world workflows: from research assistants that gather and synthesize information, to development agents that write and debug code, to business automation agents that manage multi-step processes without human intervention.
Core Concepts & Technologies
Agent Frameworks
- AutoGPT & BabyAGI
- LangGraph (State machines)
- CrewAI, LlamaIndex Agents
- Microsoft Autogen
- Custom agent architectures
Key Skills
- ReACT (Reasoning + Acting)
- Tool use & function calling
- Memory & context management
- Multi-agent collaboration
- Agent safety & guardrails
Comprehensive Curriculum
- Understanding autonomous agents vs traditional AI
- Agent architecture: Perception, Reasoning, Action
- ReACT pattern (Thought-Action-Observation loops)
- Building your first simple agent
- LLM-based agent decision making
- Setting up agent development environment
- Function calling with OpenAI API
- Building custom tools for agents
- Web search, API integration, file operations
- Tool selection and chaining strategies
- Error handling and retry logic
- Tool documentation best practices
- Graph-based agent workflows
- State machines for complex reasoning
- Conditional edges and parallel execution
- Checkpointing and persistence
- Human-in-the-loop patterns
- Building branching agent flows
- Designing agent teams and roles
- Task delegation and collaboration
- Inter-agent communication protocols
- Hierarchical vs peer-to-peer architectures
- Building research, coding, and analysis crews
- Monitoring and orchestrating agent teams
- Memory systems: Short-term, long-term, semantic
- Planning algorithms: GOAP, HTN, Monte Carlo Tree Search
- Self-reflection and self-improvement
- Agent safety, alignment, and constraints
- Performance monitoring and optimization
- Real-time agent debugging
- Capstone: Build autonomous agent system
- Project ideas: Research assistant, Code reviewer, Data analyst, Business automation
- Scalable agent infrastructure
- Cost optimization for production agents
- Monitoring, logging, and observability
- Deployment to cloud platforms
Real-World Applications
Research Agents
Autonomous research assistants that gather, synthesize, and summarize information from multiple sources
Code Generation Agents
Agents that write, test, and debug code across multiple programming languages
Data Analysis Agents
Autonomous data scientists that explore datasets, generate insights, and create visualizations
Workflow Automation
Business process automation agents that handle multi-step workflows without human intervention
Career Paths
AI Agent Developer
Average Salary: ₹12-20 LPA
Autonomous Systems Engineer
Average Salary: ₹15-25 LPA
AI Research Engineer
Average Salary: ₹10-18 LPA
ML/AI Architect
Average Salary: ₹18-30 LPA
Ready to Build Intelligent AI Agents?
Master autonomous AI agents, multi-agent systems, and workflow automation. Learn from industry experts and build real-world agentic AI applications with Dhruva Softech.
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