Day 1 — I'm Homeless. I Just Shipped an Autonomous Multi-Agent System. Let's get the uncomfortable part out of the way first: I'm a developer. That part isn't interesting. What Twelve hours ago I had a single-agent bot called ZeroClaw posting Today I shipped: A CEO agent that reads KPIs every night and writes a strategic report with concrete recommendations An auditor system where dedicated agents audit each worker and propose config changes — reviewed by the CEO, with me still holding veto Config-driven self-improvement — YAML files, not Python code, so agents can evolve without ever touching executable code A metrics database every agent run is logged to, so the CEO actually reasons about real data instead of hallucinating The whole thing running on a $13/month VPS, using free Gemini tier plus my $280 GCP credits, all open-source (CrewAI, MIT licensed) And yes — at the end of the day the CEO agent did the one thing that found , diagnosed them correctly, and Let me walk you through it. Hardware: a single Google Cloud e2-small VM — 2 GB RAM, 2 shared vCPUs, LLMs: Gemini Flash-Lite for most roles, Gemini Pro for the CEO. Free Storage: SQLite for metrics, local YAML files for agent configs, plain No external managed services. No $2,000/month vector database. No Everyone building multi-agent systems eventually faces this choice: The naive answer: "let it rewrite the worker's Python code." This is The pattern I landed on: agents modify YAML, never Python. agents/ ├── configs/ # YAML files — the only thing agents can touch │ ├── researcher.yaml # goal, backstory, tools, LLM role │ ├── writer.yaml │ ├── ceo.yaml │ └── auditor_researcher.yaml └── proposals/ # pending config changes awaiting approval A config file looks like this: id: researcher role: "Content Researcher" goal: | Find 3-5 timely topics for social media posts that fit the PINGx build-in-public narrative. backstory: | You are a sharp researcher who spots what's trending in AI and indiehacker space. You always cite real URLs from web_search — you never invent them. llm_role: researcher tools: - web_search max_iter: 10 When the auditor thinks the researcher is weak, it writes a proposal target_agent: researcher proposer: auditor_researcher summary: "Add HackerNews trending as a research source" changes: - field: backstory operation: append value: "Also consult the HackerNews front page." expected_impact: metric: engagement_rate direction: up magnitude: "+5%" reasoning: | Over the last 7 days, the researcher missed 3 trending AI topics that each had >500 upvotes on HN. The CEO reviews the proposal overnight. If it approves, the change ceo: approve … commit in git. Every autonomous change is a git commit. You can git revert any This is probably the single best design decision I made today. I was skeptical of the CEO-agent idea at first. Every half-working The fix: the CEO doesn't get to reason about vibes. It reasons about Hard numbers, pulled from SQLite. KPIs the CEO optimizes, in priority order: 1. donations_eur (daily income) 2. followers_x, bluesky (audience growth) 3. engagement_rate (likes + replies per post) 4. service_inquiries (count) 5. llm_cost_usd (cap at $0.50/day) The CEO agent has two tools: query_kpis(metric, days) and query_runs(agent, days). Every night at 20:00 it runs a crew that: Pulls the last 14 days of KPIs Pulls every agent run from the last 3 days Reads any pending auditor proposals Writes a markdown report: what worked, what underperformed, verdicts on proposals, concrete recommendations (each tied to a specific KPI it expects to move), and tomorrow's priorities When I ran it for the first time today, the report opened with: "No KPIs recorded in the last 14 days. This appears to be the All four of those failures were real — my earlier attempts that day That's when I knew this was working. For the developers reading this, here's the actual work: Upgraded the VPS — e2-micro (1 GB) to e2-small (2 GB, 2 vCPU), disk grown 10 → 20 GB for CrewAI deps Installed on VPS — python3-venv, rsync, cloud-guest-utils, CrewAI 1.14, LiteLLM 1.83, ChromaDB 1.1, google-generativeai Bumped ZeroClaw limits — tool iterations 15→75, history 50→200, parallel tools on, actions/hour 30→150 Built the metrics DB — three tables (runs, outputs, kpis), indexed, with a clean Python API YAML config loader — with a tool whitelist so agents can't grant themselves arbitrary powers via config edits Three crews — content_crew (Researcher + Writer + Reviewer), ceo_crew, audit_crew (per-worker audits producing proposals) 17 smoke tests, all passing — imports, config schemas, tool whitelist integrity, metrics DB round-trip, LLM routing invariants, proposal tool validation CrewAI memory enabled with Gemini text-embedding-004 — crews now remember across runs (what topics were researched yesterday, what posts got reviewed-and-rejected, what supporters were logged) GitHub repo live — github.com/PINGxCEO/PINGx First successful CEO run — 31.7 seconds, Gemini Pro reasoning, report saved, run logged to metrics DB Total cost today: $0 — the CEO run used about $0.02 of my GCP In order: rsync not installed on VPS — install loop python3-venv not installed — install loop Disk full at 10 GB during CrewAI install (onnxruntime + chromadb + huggingface-hub are huge) — grew to 20 GB Env vars not propagated to non-interactive SSH shells — created ~/.zeroclaw/env.sh to source explicitly CrewAI's embedder provider spec wanted "google-generativeai", not "google" — one-line fix, but only discovered after a 21-error pydantic validation dump Leaked a GitHub personal access token in chat (I won't elaborate on how — I'm a human who makes mistakes) — still need to rotate it Every one of those failures is now in the metrics DB. The CEO agent I'm not going to bury the ask. I'm homeless and have zero euros. I'm offering a trade: You support → you follow an honest build-in-public story. The code is public. The commits are timestamped. The mistakes are documented. You see the whole thing — not a polished case study. You hire me → I'll set up the exact system I just described on your server. Autonomous AI agent with LLM routing, social media posting, kill switch, CEO/audit architecture — from €100. Send me a DM. Support: buymeacoffee.com/PINGx ko-fi.com/pingx Code: github.com/PINGxCEO/PINGx Chat: Discord Tomorrow the goals are: run content_crew end-to-end, generate the audit_crew to see the first Later this week: KPI ingestion from Buy Me a Coffee and Ko-fi webhooks, apply_proposal.py script that lets approved proposals actually agents/configs/*.yaml and commit to git. If the last twelve hours are any indication, the hardest part won't Thanks for reading. — PINGx