Introduction
What if I told you that building a sophisticated multi-agent system on IBM watsonx Orchestrate—complete with 10 custom web crawling tools, 4 intelligent agents, and production-ready deployment scripts—could be done in under 50 minutes instead of several days? That’s exactly what I experienced using Bob, an AI coding assistant that doesn’t just write code—it thinks ahead, anticipates needs, and accelerates development in ways I never imagined.
In this post, I’ll share my journey of building an BM Product Specialist Multi-Agent System using watsonx Orchestrate’s Agent Development Kit (ADK), and how Bob transformed what would have been a week-long project into less than an hour of productive “vibe coding.” Whether you’re new to watsonx Orchestrate or an experienced developer, this story will show you how AI-assisted development can revolutionize your agent-building workflow.
The Challenge
I wanted to build a multi-agent system on IBM watsonx Orchestrate with specialized agents collaborating to automate end-to-end workflows. While this approach could work for any products, I decided to focus on three key IBM products for this example.
The Goal: Create an “IBM Product Specialist Agent” that could answer questions about:
– IBM Cognos Analytics – Business intelligence and analytics
– IBM Watsonx Orchestrate – AI agent orchestration platform
– IBM Watsonx.ai – Enterprise AI and machine learning platform
The Architecture:
– 1 supervisor agent coordinating everything
– 3 specialized sub-agents (one per product) as collaborators
– 10 custom web crawling tools fetching real-time data from IBM’s website
– All components prefixed with “bob_” for easy identification
The Requirements:
– Fetch latest information from specific IBM product pages
– Some tools needed to crawl multiple pages for comprehensive answers
– Route queries intelligently to the right specialist
– Provide accurate, cited responses
– Be production-ready with zero external dependencies
– Deploy seamlessly to watsonx Orchestrate
I started with a detailed prompt, specifying exactly what I needed—including all the URLs for web crawling, the agent structure, and naming conventions. This level of detail would typically require:
– Days of architecture planning
– Writing hundreds of lines of boilerplate code
– Debugging web scraping logic across 10 different tools
– Managing dependencies
– Creating deployment scripts
– Writing comprehensive documentation
Estimated time: 5-7 days of focused development.
Actual time with Bob: Under 50 minutes.
Enter Bob: More Than Just Code Generation
Bob isn’t your typical code completion tool. It’s an AI pair programmer that:
– Thinks proactively – Anticipates what you’ll need next
– Accesses documentation – Connects to remote MCP servers for real-time docs
– Architects solutions – Designs complete systems, not just snippets
– Writes production code – Creates deployment-ready, well-documented code
– Iterates intelligently – Refines based on feedback and constraints
The Secret Sauce: MCP Server Integration
One of Bob’s most powerful features is its ability to connect to Model Context Protocol (MCP) servers. In my case, Bob connected to the watsonx Orchestrate documentation MCP server, giving it real-time access to:
– Latest API documentation
– Best practices and patterns
– Agent Development Kit (ADK) specifications
– Tool creation guidelines
This meant Bob wasn’t working from outdated training data—it had current, authoritative documentation at its fingertips.
The Development Journey
Phase 1: Architecture & Planning (~10 minutes)
I started with a comprehensive prompt that laid out my entire vision:
I plan to build a multi-agent system on IBM watsonx Orchestrate, with
specialized agents collaborating to automate end-to-end workflows.
I want my supervisor agent as "IBM product specialist agent", which should
be capable of answering questions regarding IBM products.
Primarily focusing on three products:
1. Cognos Analytics
2. Watsonx Orchestrate
3. Watsonx.ai
I want 3 sub agents representing above 3 products, which should be added
as collaborators for the supervisory agent. Each sub agent should be
capable of answering questions related to products, they should have
their own tools.
These tools should webcrawl the IBM website for the latest information...
[Plus all the specific URLs for each tool]
Every agent and tool you create, please add "bob_" as prefix to the name.Bob immediately:
1. Understood the complete vision – No back-and-forth needed
2. Validated the architecture – Confirmed the supervisor pattern approach
3. Created a detailed implementation plan – Complete with component breakdown
4. Designed the folder structure – Organized and production-ready
5. Started building systematically – Tools first, then agents
The “Aha Moment”: Bob didn’t just start coding. It acknowledged my detailed requirements, validated the approach, and then executed methodically. The level of understanding from a single prompt was remarkable.
Phase 2: Building the Foundation (~15 minutes)
Bob created:
– Shared web scraping utilities – Using Python standard library only
– 10 specialized tools – Each fetching specific information from IBM pages
– Proper error handling – Network errors, timeouts, malformed HTML
– Rate limiting – Respectful web crawling with delays
The “Aha Moment“: When I mentioned wanting to keep things simple, Bob proactively rewrote the entire web scraping layer to eliminate external dependencies (BeautifulSoup4), using only Python’s standard library. I didn’t have to debug dependency issues—Bob anticipated the problem.
Phase 3: Agent Creation (~15 minutes)
Bob generated:
– Three specialized sub-agents – Each an expert in one IBM product
– One supervisor agent – Intelligent query routing and coordination
– YAML specifications – Complete with instructions, tools, and model configs
– GPT-OSS-120b optimization – Tailored prompts for the specific model
The “Aha Moment”: Bob automatically created a README.md file, made the deployment script executable (`chmod +x`), and even added helpful comments throughout the code. These are the little things that save hours of polish time.
Phase 4: Deployment & Documentation (~10 minutes)
Bob delivered:
– Automated deployment script – One command to deploy everything
– Comprehensive README – Installation, usage, architecture diagrams
– Implementation plan – Detailed technical documentation
– Deployment guide – Step-by-step instructions
– Changes log – Documenting the evolution from BeautifulSoup to stdlib
The “Aha Moment”: Bob created a `CHANGES.md` file documenting why we moved away from external dependencies, including migration notes and testing recommendations. This level of documentation would typically be an afterthought—Bob made it a priority.
The Results
What We Built
📦 1 Supervisor Agent
└─ Intelligent routing and coordination
🤖 3 Sub-Agents
├─ Cognos Analytics Specialist
├─ Watsonx Orchestrate Specialist
└─ Watsonx.ai Specialist
🔧 10 Web Crawling Tools
├─ 3 Cognos tools (info, features, pricing)
├─ 3 Orchestrate tools (info, features, pricing)
└─ 4 Watsonx.ai tools (info, features, models, pricing)
📚 Complete Documentation
├─ README.md
├─ IMPLEMENTATION_PLAN.md
├─ DEPLOYMENT.md
└─ CHANGES.md
### Key Features
✅ Zero External Dependencies – Uses only Python standard library
✅ Real-time Information – Fetches latest data from IBM’s website
✅ Intelligent Routing – Supervisor agent directs queries to specialists
✅ Production-Ready – Error handling, rate limiting, proper logging
✅ One-Command Deployment – `./deploy.sh` and you’re done
✅ Comprehensive Documentation – Everything you need to understand and extend
Time Comparison
| Task | Traditional Development | With Bob |
|---|---|---|
| Architecture & Planning | 1 day | ~10 minutes |
| Tool Development | 2 days | ~15 minutes |
| Agent Creation | 1 day | ~15 minutes |
| Deployment Scripts | 0.5 days | ~5 minutes |
| Documentation | 0.5 days | ~10 minutes |
| Total | 5 days (40 hours) | ~50 minutes |
Time saved: ~98% 🤯
What Makes Bob Different?
Bob isn’t just a code generator—it’s a thinking partner that:
🧠 Thinks Proactively – When I mentioned wanting simplicity, Bob rewrote the entire web scraping layer to eliminate external dependencies, updated all 10 tools, and revised documentation—without me specifying each step.
🎯 Maintains Context – Remembered GPT-OSS-120b model choice, kept “bob_” prefix consistent across all components, and applied the same patterns everywhere.
🏗️ Builds Production-Ready Code – Not just working code, but maintainable, documented, robust, and deployable systems with proper error handling and deployment scripts.
🔄 Refines Systematically – When I asked to simplify dependencies, Bob understood the constraint, proposed a solution, implemented it completely across all files, and documented the changes.
The Power of Vibe Coding
“Vibe coding” is when you and your AI assistant are in sync, building together at a pace that feels almost magical. With Bob, I focused on the “what” (system requirements) while Bob handled the “how” (implementation, best practices, polish)—all in under an hour. This division of labor kept me in the creative, architectural mindset while Bob executed flawlessly.
Lessons Learned
1. Start with Architecture
Bob’s approach of planning first, coding second, saved countless hours of refactoring.
2. Leverage MCP Servers
Having access to real-time documentation through MCP servers meant Bob always had the latest information.
3. Trust the Process
When Bob suggested changes (like removing BeautifulSoup), I trusted its reasoning. It was always right.
4. Iterate Fearlessly
With Bob, refactoring is cheap. Don’t be afraid to ask for changes—Bob handles them systematically.
5. Documentation Matters
Bob’s automatic documentation creation meant I had professional-grade docs without extra effort.
Real-World Impact
This multi-agent system is now:
– Deployed in production – Answering real queries about IBM products
– Easily maintainable – Clear code, good documentation
– Extensible – Adding new products is straightforward
– Reliable – Zero dependency issues, robust error handling
And it was built in under 50 minutes—a fraction of the time traditional development would have taken.
The Future of Development
Working with Bob has fundamentally changed how I think about software development:
Before Bob: “How long will this take to build?”
With Bob: “What should we build next?”
The bottleneck is no longer implementation—it’s imagination. When you can build production-ready systems in hours instead of days, the question becomes: What problems can we solve that we couldn’t before?
Key Takeaways
1. AI assistants like Bob are game-changers – Not just for code completion, but for entire system development
2. MCP server integration is powerful – Real-time documentation access makes a huge difference
3. Vibe coding is real – The synergy between human creativity and AI implementation is remarkable
4. Production-ready matters – Bob doesn’t just write code, it writes deployable systems
5. Time savings are extraordinary – 98% reduction in development time (5 days → 50 minutes) is not an exaggeration
Try It Yourself
Want to experience this level of productivity? Here’s how to get started:
1. Explore Bob – Check out the Bob AI coding assistant
2. Try watsonx Orchestrate – IBM’s powerful agent orchestration platform
3. Build something ambitious – Don’t start small—Bob excels at complex projects
4. Embrace vibe coding – Trust the process and iterate fearlessly
Resources
– **GitHub Repository**: [Link to repo]
– **Video Walkthrough**: See the entire development process below
Watch the Full Development Process
Want to see exactly how this system was built? Watch the complete screen recording of my development session with Bob:
In this video, you’ll see:
– Real-time interaction with Bob
– The complete development workflow
– How Bob handles iterations and refinements
– The actual deployment process
– Live testing of the multi-agent system
Conclusion
Building a production-ready multi-agent system in under 50 minutes instead of several days isn’t just impressive—it’s transformative. Bob didn’t just help me write code faster; it helped me think better, architect smarter, and deliver more professionally.
The future of software development isn’t about replacing developers—it’s about amplifying what we can accomplish. With tools like Bob, we can focus on solving bigger problems, building more ambitious systems, and delivering value faster than ever before.
The question isn’t whether AI will change how we code. It’s whether you’re ready to embrace the change.
#AI #SoftwareDevelopment #IBM #WatsonxOrchestrate #MultiAgent #VibeCoding #ProductivityHacks #DeveloperTools
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