Looking for an AI Architect (OpenClaw/Moltbot): assemble a "super-sub" 24/7 (1 brain + swarm of agents)

Hello! I have reviewed your assignment and believe that I can successfully complete it. I would be happy to collaborate, please write to me personally for further details.

Hi there,

Your vision is not a chatbot but a real autonomous execution system, and that’s exactly how I would approach it: a Supervisor/Orchestrator agent built on an OpenClaw/Moltbot-compatible architecture, managing a structured pool of worker agents (Content, Research, Coding, QA, Auditor, Trend Hunter, Budget Controller). The supervisor handles goal decomposition, task graph creation, priority scoring, budget awareness, retries/timeouts, and parallel execution, while each worker operates in a constrained, role-specific sandbox with structured input/output schemas and mandatory self-review before escalation.

Beyond your requirements, I would introduce a Decision Ledger (JSONL-based reasoning + outcome tracking), a Skill Registry (controlled capability expansion only after your approval), and a Cost-Aware Model Router that dynamically switches between cheap/balanced/premium models based on task criticality, historical failure rate, and expected ROI. For the AI Influencer Factory, I’d design character-specific micro-agents with persistent persona memory, content pipelines (ideation → draft → QA → style alignment → scheduling), and analytics feedback loops to refine tone and engagement automatically. For Builder + Trend Hunter, I’d implement a signal ingestion layer (APIs, scraping, trend clustering), automated MVP scaffolding (templates + code-gen workers), and a weekly strategic synthesis report generated by a planning agent—not just raw alerts.

My approach is stronger than standard implementations because it treats quality control as a first-class system layer (self-check → peer-check → auditor → confidence score), not an afterthought, and integrates strict budget governance at the orchestration level to prevent token leaks and recursive loops. Token and infrastructure costs are reduced through hierarchical task splitting, early cheap-model filtering, caching embeddings/results, memory pruning with retention policies, and failure-pattern learning to avoid repeated low-quality generations.

Security will follow your constraints: SSH-key-only access, containerized deployment via Docker Compose, isolated services, secret management through environment vaulting, temporary developer access with full revocation, and a complete handover package (repo, runbook, backup/restore, permission schema).
…
Estimated roadmap:
Phase 1 (3–4 weeks): Core orchestrator + worker framework + budget router + logging + basic demo scenarios.
Phase 2 (4–6 weeks): Influencer Fact

Good day!
I have experience in AI architecture and multi-agent systems, including OpenClaw/Moltbot, OpenRouter/Syntx. I will develop your "super-replacement" with quality control, cost optimization, and complete security.

Write to me in private messages, we will clarify the details.

Show original

Translate