Show & Tell: AliBaba — 6-step AI brain managing 9 agents autonomously

[jivaniproject110-pixel]

Hey everyone — sharing an architecture that might be useful if you're working on multi-agent coordination.

I built AliBaba, a central intelligence brain that manages 9 specialized agents running autonomously 24/7. The core insight was replacing manual coordination with a 6-step pipeline + shared intelligence hub.

The pipeline

GATHER → reads 50+ JSON intelligence files (news, performance, health)
ANALYZE → Groq LLM identifies patterns and what's working
THINK → maps every decision to one measurable goal
DECIDE → emits JSON instructions to each agent
ADVISE → daily summary brief
LEARN → adjusts agent priority scores based on actual results

The intelligence sharing layer

Every agent writes structured JSON after every task to a shared hub:

/alibaba/intelligence/
  ├── web3/       ← bounty platform outcomes
  ├── content/    ← what's getting views
  ├── bounties/   ← submission win/loss data
  └── market/     ← trending topics

AliBaba reads all categories every cycle. The observation window grows without retraining — just more data.

Self-learning

Each income stream has a weekly score updated from real performance:

# Score shifts automatically:
# "superteam_bounty": 0.88 → 0.91  (2 submissions won)
# "adsense_blog": 0.72              (pending AdSense approval)

Agents with higher scores get more resources next cycle.

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Happy to answer questions about the orchestration layer or the self-review system for the bounty agent. Full write-up + live dashboard: https://agents.v-architect.tech

[aasimansari1]

Love these builds! Adding mine: ML/AI Interview Prep — a comprehensive open-source repo with 500+ Q&A for ML engineer interviews. Covers RAG architecture, LlamaIndex patterns, transformer internals, PyTorch, scikit-learn pipelines, and ML system design. Built for engineers preparing for roles at AI labs. Would appreciate a ⭐ if it helps!