Multi-Agent Systems: Design & Orchestration
Design systems where several specialised agents collaborate: orchestration patterns, communication, evaluation and governance.
- ✓ Instructor-led live sessions, online or in Bengaluru
- ✓ Hands-on labs in every module
- ✓ Capstone project with trainer review
- ✓ Curriculum customisable for corporate teams
- ✓ Certificate of completion
Next batch: Sat, 21 Nov 2026 · 32 hours · 4 days
Multi-Agent Systems: Design & Orchestration
Duration: 32 hours · 4 days
Next batch: Sat, 21 Nov 2026
Format: Online live · Bengaluru classroom
Modules: 9 · Topics: 42
Level: Advanced
Fee: ₹14,999
Trusted by leading experts
Upcoming batches
Open batches for individuals; corporate batches on your dates.
| Programme | Starts | Mode | Schedule | Duration | |
|---|---|---|---|---|---|
| Multi-Agent Systems: Design & Orchestration | Sat, 21 Nov 2026 | Online live | Weekend (Sat) | 5 Saturdays | Enquire |
| Multi-Agent Systems: Design & Orchestration | Mon, 04 Jan 2027 | Bengaluru classroom | Weekday | 4 days | Enquire |
| Multi-Agent Systems: Design & Orchestration | Sat, 13 Feb 2027 | Online live | Weekend (Sat) | 5 Saturdays | Enquire |
Soaring demand, and where it takes you
As agent projects grow more ambitious, architects who understand multi-agent design and its risks are increasingly needed.
Become an expert
Roles this programme prepares you for:
Become a AI Architect
Designs multi-agent architectures and their governance.
- Decide when multi-agent designs are justified
- Apply supervisor, hierarchical and sequential patterns
- Design shared state and inter-agent messaging
About the course
An advanced course on systems in which multiple specialised agents collaborate: orchestration patterns, shared state and messaging, tool access through the Model Context Protocol, failure modes, evaluation and governance. Aimed at senior developers and architects.
Design systems where several specialised agents collaborate: orchestration patterns, communication, evaluation and governance.
Why should I attend?
Senior developers
AI and solution architects
Platform engineers
Technical leads of AI teams
Prerequisites
Experience building at least one single-agent application; Python
Level
Advanced
Delivery modes, duration and fee
Online live
Instructor-led virtual classroom; weekday (full-day) or weekend (Saturday) batches
Bengaluru classroom
In-person batches at our Bengaluru training venue
On-site at your office
Private corporate batch anywhere in India, scheduled around your teams
Blended
Live sessions plus guided self-paced labs and mentor check-ins
Duration: 32 hours · 4 days
Course fee (open batch): ₹14,999
Corporate / team batches: per-participant or flat batch pricing on request, based on group size, format and customisation.
What will you learn
- Decide when multi-agent designs are justified
- Apply supervisor, hierarchical and sequential patterns
- Design shared state and inter-agent messaging
- Integrate tools and data through MCP
- Detect and prevent loops, cost blow-ups and injection
- Evaluate and govern multi-agent systems
Skills you will gain
Architecture
Integration
Operations
Tools and platforms covered
Python
LangGraph
CrewAI
Model Context Protocol
OpenAI API
DockerCourse curriculum
M01 Single vs multi-agent
- Trade-offs
- Complexity costs
- Decision guide
- Cost and latency modelling
- Reference designs
🧪 Hands-on: Analyse three scenarios
M02 Orchestration patterns
- Supervisor
- Hierarchical teams
- Sequential and parallel
- Debate and critique patterns
- Market and auction patterns
🧪 Hands-on: Implement a supervisor pattern
M03 State and communication
- Shared state
- Message passing
- Handoffs
- Blackboard architectures
- Context sharing limits
🧪 Hands-on: Build agent handoffs
M04 Tools via MCP
- MCP concepts
- Building an MCP server
- Securing tool access
- Agent-to-agent protocols overview
- Remote MCP servers
🧪 Hands-on: Expose a tool via MCP
M05 Failure modes
- Loops and runaway cost
- Conflicting agents
- Prompt injection
- Deadlock and loop detection
- Budget caps
🧪 Hands-on: Break and fix a system
M06 Evaluation and governance
- System-level tests
- Tracing
- Approval and audit
- Approval workflows
- Compliance evidence
🧪 Hands-on: Evaluate and add an audit trail
M07 Building a supervisor system
- Designing specialist agents
- Routing logic
- Aggregating results
- Testing handoffs
🧪 Hands-on: Build a supervisor with three specialist agents
M08 Scaling and operations
- Concurrency
- Queues and retries
- Observability across agents
- Cost dashboards
🧪 Hands-on: Instrument a multi-agent system end to end
M09 Capstone: multi-agent workflow
- Design document
- Build
- System evaluation
- Review
🧪 Hands-on: Deliver a governed multi-agent workflow for a business process
Hands-on projects
Multi-agent analyst
A supervisor coordinating research, analysis and writing agents with human approval.
Why train with us
Built around your stack
Labs and examples adapted to the tools, cloud and data your team actually uses.
Practice-first
Every module ends in a lab, and the course ends with a reviewed capstone.
Practitioner-led
Delivered by Karthick Selvam and vetted specialist trainers from our network.
Visible progress
Attendance, lab completion and assessment results shared with your L&D team.
Certificate of completion
Participants who complete the labs and capstone receive a Learning Dots certificate of completion for Multi-Agent Systems: Design & Orchestration. [confirm certificate wording / sample image]
Learner reviews
“[Verified learner review for this course]”
“[Verified learner review for this course]”
Meet your trainer
Karthick Selvam
Founder · Data Engineering Corporate Trainer, Bengaluru
[Trainer bio, experience and areas of expertise]
Frequently asked questions
What experience is needed?
Experience building at least simple LLM applications or agents is recommended.
Can the curriculum be customised for our team?
Yes. Modules, depth, duration and labs can be tailored to your stack and goals.
What delivery formats are available?
Online live instructor-led sessions, Bengaluru classroom, or on-site at your office.
How do we enquire or book?
Use the enquiry form on this page or the Enquire button next to any batch.
What does it cost?
The open-batch fee is ₹14,999 per participant plus GST. Corporate batches are priced on group size, format and customisation.
How long is the course?
32 hours · 4 days. Corporate batches can be split into half-day sessions.
A closer look
About this course
An advanced course on systems in which multiple specialised agents collaborate: orchestration patterns, shared state and messaging, tool access through the Model Context Protocol, failure modes, evaluation and governance. Aimed at senior developers and architects.
What is Multi-Agent Systems: Design & Orchestration?
Design systems where several specialised agents collaborate: orchestration patterns, communication, evaluation and governance.
Who this course is for
Senior developers, AI and solution architects, Platform engineers, Technical leads of AI teams.
What you will be able to do
Decide when multi-agent designs are justified. Apply supervisor, hierarchical and sequential patterns. Design shared state and inter-agent messaging. Integrate tools and data through MCP. Detect and prevent loops, cost blow-ups and injection. Evaluate and govern multi-agent systems.
Career paths
Roles this course supports include AI Architect, Senior Agent Engineer, AI Platform Lead. As agent projects grow more ambitious, architects who understand multi-agent design and its risks are increasingly needed.
Related courses
Agentic AIAgentic AI with LangChain & LangGraph
Build tool-using agents and stateful workflows with LangChain and LangGraph, with evaluation and guardrails.
Agentic AIBuilding AI Agents with LangGraph & CrewAI
Build tool-using agents and agent teams with two popular open-source frameworks, and learn when to use each.
Agentic AI for Business Process Automation
Identify, design and pilot agent-driven automations for operations, support and back-office processes, safely.
AI & GenAI guides
Generative AI vs Traditional AI: What Is the Difference?
Traditional AI mostly predicts or classifies. Generative AI produces new content. Both have a place in the enterprise.
What Is Prompt Engineering?
Prompt engineering is the practice of writing instructions that get reliable, useful output from a language model.
What Is ChatGPT and How Does It Relate to Other AI Assistants?
ChatGPT is a conversational AI assistant from OpenAI, built on large language models. It is one of several similar assistants.
How to Compare AI Assistants for Your Team
Several general-purpose AI assistants compete for enterprise use. Rather than chasing rankings, evaluate them against your own needs.