The Problem: 5 AI Agents = Complete Chaos
Ever tried running multiple AI agents together? Here's what happens:
- Agent A analyzes data
- Agent B rewrites everything from scratch (doesn't know what A found)
- Agent C duplicates A's work
- You become a human copy-paste machine between ChatGPT windows
- Your API bill explodes
I burned through $3,000 learning this the hard way.
The Solution: AI Team Orchestrator
I built a framework that orchestrates AI agents like a real company:
How It Works
Your goal: "Increase Instagram engagement by 40%"
What happens behind the scenes:
- Director Agent analyzes and assembles team
- Marketing Strategist creates strategy
- Content Creator receives strategy context (no duplication!)
- Data Analyst tracks metrics
- All agents share workspace memory
Key Architecture Decisions
1. Conditional Quality Gates (94% cost savings)
Instead of checking everything:
- Frontend-only changes: skip backend validators (saves $0.23 per check)
- Database changes: trigger all validators (full validation when needed)
2. Agent Handoffs with Context
Agents pass context like Slack messages:
- From: ResearchAgent
- To: StrategyAgent
- Context: "Found 3 key competitor patterns"
- Artifacts: ["analysis.json", "data.csv"]
3. Workspace Memory (No repeated work)
Semantic memory prevents re-doing tasks:
- If similar task found: use previous approach
- If new task: execute and learn
Real Production Metrics
| Metric | Before | After |
|---|---|---|
| API Costs | $240/month | $3/month |
| Task Recovery | Manual | <60s autonomous |
| Context Retention | 12% | 89% |
| Setup Time | 2 days | 15 minutes |
| Error Rate | 23% | 1.2% |
| Throughput | 2.3/sec | 8.7/sec |
Tech Stack
- Backend: FastAPI + OpenAI Agents SDK
- Frontend: Next.js 15 + TypeScript
- Database: Supabase
- Architecture: Blackboard pattern with Pydantic contracts
Get Started
git clone https://github.com/khaoss85/AI-Team-Orchestrator
cd ai-team-orchestrator
./scripts/quick-setup.sh
What I Need From The Community
This isn't a finished product - it's a starting point. Looking for:
- Test it with your use cases
- Report what breaks (it will break)
- Suggest improvements based on real needs
- Contribute if you want to
The roadmap is completely open. Your use case = our next feature.
Lessons Learned (The Hard Way)
Documented everything in a 62,000-word guide:
- Why agents create infinite loops (5,000 tasks in 20 minutes!)
- Race conditions with parallel agents
- Why agents don't use tools even when available
- The $40 CI test that forced us to build mock providers
Example: The Infinite Loop Problem
What went wrong:
- Agent decomposes task
- Each subtask gets decomposed again
- No depth limit = infinite recursion
- 5,000 tasks created in 20 minutes
The fix:
- Hard depth limit (MAX_DEPTH = 5)
- AI decides if task is atomic
- Anti-loop counter at workspace level
Architecture Deep Dive
The system uses a multi-layer architecture:
Layer 1: Input Processing
- User Input → Goal Engine → Task Planner
Layer 2: Execution
- Agent Team → Task Executor → Deliverable Generator
Layer 3: Optimization
- Memory & Learning → Quality Assurance → Improvement Loop
Each layer feeds back into the system, creating continuous improvement.
Real-Time Thinking Process (Claude/o3 Style)
You can watch agents think in real-time:
[THINKING] Breaking down objective into sub-goals
[ANALYZING] Identifying required specialist skills
[MEMORY CHECK] Found 3 similar patterns from workspace #42
[DECISION] Assembling team of 4 specialists...
[HANDOFF] Marketing strategy completed
[CONTEXT PASSED] 3 key insights from research
[CONFIDENCE] 92%
Current Limitations
Being transparent about what needs work:
✅ What works:
- Basic multi-agent orchestration
- Memory system and context retention
- Cost optimization through quality gates
- Handoff mechanism
🚧 What needs improvement:
- Error recovery patterns
- Performance with 10+ agents
- Better debugging tools
- More sophisticated memory retrieval
Join The Discussion
What's your biggest multi-agent orchestration challenge? Let's solve it together.
Links:
*If this helped you save on API costs or solve orchestration problems, consider starring the repo!
Top comments (1)
This is a masterclass in turning chaos into coordination. The orchestration framework not only tackles the fragmentation of multi-agent workflows it reframes the problem entirely. Context handoffs, semantic memory, and quality gates aren’t just clever optimizations they’re architectural commitments to efficiency and clarity.
The blackboard pattern with Pydantic contracts is a particularly elegant touch. It enforces structure without sacrificing flexibility, and the real-time thinking logs offer transparency that’s rare in agent systems. That 94% cost reduction isn’t just impressive it’s a signal that thoughtful design beats brute-force scaling.
Appreciate the openness around limitations and the invitation to co-create. This feels less like a tool drop and more like the beginning of a movement toward sustainable agent orchestration.