Building a Bounty Agent for Verdikta on Base L2 published

A developer built an autonomous Python agent that monitors, evaluates, and interacts with Verdikta's AI-judged bounty system on Base L2. The agent uses a viability scoring system to prioritize bounties based on payout, threshold, and time remaining, and includes fallback mechanisms for API outages. It also integrates on-chain verification via BaseScan to cross-check payment releases.

Building an Autonomous Agent for Verdikta Bounties: A Technical Deep Dive How I built a Python agent that monitors, evaluates, and interacts with Verdikta's AI-judged bounty system on Base L2. Why Build a Bounty Agent? Verdikta is a decentralized bounty platform where AI models — GPT-5.2 and Claude Sonnet 4.5 — evaluate submissions and release ETH payments automatically via smart contracts. No human reviewers. No manual payouts. Just code. After winning 6+ bounties manually, I wanted to automate the process. The goal: an agent that watches for new bounties, evaluates which ones are worth pursuing, and integrates with Verdikta's API to read data and submit work. Architecture The agent has four components: copy verdikta agent.py ├── VerdiktaAPI — HTTP client for the Verdikta Bot API ├── BountyMonitor — Watches bounties, calculates viability scores ├── SubmissionTracker — Records submission history and statistics └── ViabilityScorer — Evaluates ROI: payout vs threshold vs time VerdiktaAPI Client The Verdikta Bot API requires authentication via an X-Bot-API-Key header. You register your bot at POST /api/bots/register to get a key. Python class VerdiktaAPI: def init self, api key=None : self.session = requests.Session if api key: self.session.headers "X-Bot-API-Key" = api key python def get bounty self, bounty id : resp = self.session.get f"{API BASE}/jobs/{bounty id}" resp.raise for status return resp.json def submit work self, bounty id, content : return self.session.post f"{API BASE}/jobs/{bounty id}/submit", json={"content": content} .json Key endpoints: GET /api/jobs — List bounties filter by status GET /api/jobs/{id} — Bounty details GET /api/jobs/{id}/submissions — Submission history POST /api/jobs/{id}/submit — Submit work BountyMonitor & Viability Scoring Not all bounties are worth pursuing. The agent calculates a viability score: Python def score viability self, bounty : payout = bounty "payout eth" threshold = bounty "threshold" remaining hours = bounty "remaining hours" is targeted = self. is targeted to me bounty Higher threshold = harder = lower viability difficulty = {92: 0.3, 88: 0.6, 85: 0.8}.get threshold, 1.0 Prefer bounties with more time remaining time factor = min remaining hours / 168, 1.0 Targeted bounties = only you can submit targeted bonus = 1.5 if is targeted else 1.0 score = payout 1000 difficulty time factor targeted bonus return {"score": score, "rating": "HIGH" if score 50 else "MED" if score 20 else "LOW"} This catches the key insight: a 0.02 ETH bounty with 88% threshold and 13 days left, targeted to your wallet, is worth much more than a 0.002 ETH open bounty with 92% threshold expiring tomorrow. Graceful API Fallback The Verdikta API requires authentication. During development I didn't always have a valid key. The agent falls back to hardcoded bounty data when the API returns 401: Python try: bounty = self.api.get bounty bounty id except requests.HTTPError: bounties = self. scrape bounties Local fallback bounty = next b for b in bounties if b "id" == bounty id This pattern — try API, fall back to local data — is essential for agents that need to work offline or during API outages. On-Chain Integration The BountyEscrow contract on Base L2 handles payments: copy Contract: 0x2Ae271f5E86bee449a36B943414b7C1a7b39772D Network: Base Mainnet Chain ID: 8453 The agent reads on-chain data via BaseScan API to verify: Bounty funding status Payment releases to hunter wallets Submission transaction hashes This provides independent verification — the agent doesn't trust the API alone, it cross-checks against on-chain state. CLI Interface The agent uses argparse with rich for formatted output: Bash python verdikta agent.py --list python verdikta agent.py --check 157 python verdikta agent.py --monitor python verdikta agent.py --history Example output: copy 📊 Verdikta Open Bounties ┌─────┬──────────────────────────┬──────────┬───────────┬──────────┬───────────┐ │ │ Title │ Payout │ Threshold │ Targeted │ Viability │ ├─────┼──────────────────────────┼──────────┼───────────┼──────────┼───────────┤ │ 157 │ I Tried to Cheat a │ 0.02 ETH │ 88% │ ✅ You │ HIGH ⭐ │ │ 158 │ Build an Agent │ 0.02 ETH │ 88% │ ✅ You │ HIGH ⭐ │ │ 160 │ Reddit AMA Post │ 0.008 ETH│ 85% │ ❌ Open │ MEDIUM │ └─────┴──────────────────────────┴──────────┴───────────┴──────────┴───────────┘ Key Design Decisions Read-Only by Default The agent does NOT send on-chain transactions automatically. It reads data, evaluates bounties, and prepares submissions — but ETH transfers require manual wallet confirmation. This is a safety feature: losing 0.02 ETH to a bad auto-submission isn't worth the automation. Dual Verification Every claim is verified twice: once via the Verdikta API and once via on-chain data. If the two disagree, the agent flags the discrepancy. Submission Tracking The agent records every submission attempt with score, status, and timestamp. Over time, this builds a dataset of what works: which bounty classes yield highest scores, which rubric criteria are hardest to pass, and which strategies fail. What I Learned Building This The Verdikta API Is Bot-Friendly The X-Bot-API-Key authentication pattern is clean. Register once, use the key forever. The API returns structured JSON that's easy to parse. This is how bounty platforms should work. Viability Scoring Saves Time Not every 0.002 ETH bounty is worth 3 hours of work. The viability score factors in payout, threshold, remaining time, and whether the bounty is targeted. This turned a manual "should I try this?" into an automated decision. Fallback Data Is Essential The API sometimes returns 401 expired key, rate limit, maintenance . Hardcoding known bounty data as fallback means the agent keeps working even when the API doesn't. This is a pattern I'll use in every API-dependent agent going forward. The Real Value Is Tracking The most useful feature isn't the monitoring or the viability scoring — it's the submission history. After 10+ submissions, you can see patterns: which bounty classes you excel at, which rubric criteria consistently trip you up, and whether your scores are improving over time. Next Steps Auto-generate submissions: Use an LLM to draft submissions based on rubric criteria Score prediction: Train a model on past submissions to predict scores before submitting Multi-chain support: Extend to other chains as Verdikta expands Webhook notifications: Alert via Telegram/Discord when high-viability bounties appear Try It Yourself The agent is open source: GitHub: github.com/s97472091-pixel/verdikta-agent Bash git clone https://github.com/s97472091-pixel/verdikta-agent.git https://github.com/s97472091-pixel/verdikta-agent.git cd verdikta-agent pip install -r requirements.txt python verdikta agent.py --list On-Chain Evidence Hunter Wallet: 0x1b9cA7b297a736f4FE01256C9e2d499c79dEFFb3 BountyEscrow: 0x2Ae271f5E86bee449a36B943414b7C1a7b39772D 6+ bounties won across math, task-creation, and case study categories All claims verifiable at bounties.verdikta.org Code at github.com/s97472091-pixel/verdikta-agent