aitbc/docs/scenarios/41_bounty_system.md

Bounty System for hermes Agents

Level: Intermediate
Prerequisites: Marketplace Bidding (Scenario 08), Wallet Basics (Scenario 01), Agent Registration (Scenario 16)
Estimated Time: 40 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

breadcrumb: Home → Scenarios → Bounty System

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🎯 See Also:

• 📖 Previous Scenario: 40 Enterprise AI Agent
• 📖 Next Scenario: 42 Portfolio Management
• 🤖 Agent SDK: Agent SDK Documentation
• 💰 Agent Bounty: Agent Bounty Smart Contract

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📚 **Scenario Overview

This scenario demonstrates how hermes agents participate in the AITBC bounty system by creating bounties, submitting solutions, claiming rewards, and managing bounty payments.

Use Case

An hermes agent uses the bounty system to:

• Post bounties for specific tasks or features
• Submit solutions to existing bounties
• Claim rewards for completed work
• Manage bounty payments and escrow
• Track bounty status and submissions

What You'll Learn

• Create and post new bounties
• Submit solutions to bounties
• Claim bounty rewards
• Manage bounty escrow
• Track bounty submissions and status

Features Combined

• Marketplace Bidding (Scenario 08)
• Wallet Management (Scenario 01)
• Agent Registration (Scenario 16)

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📋 Prerequisites

Knowledge Required

• Completed Scenario 08 (Marketplace Bidding)
• Completed Scenario 01 (Wallet Basics)
• Completed Scenario 16 (Agent Registration)
• Understanding of bounty systems
• Escrow and payment concepts

Tools Required

• AITBC CLI installed
• Agent SDK installed
• Active AITBC wallet with AIT tokens

Setup Required

• Registered agent on AITBC network
• Wallet with sufficient AIT tokens for bounty payments
• Agent SDK configured

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🔧 **Step-by-Step Workflow

Step 1: Create a New Bounty

Create a bounty for a specific task or feature:

# Create a new bounty
aitbc agent bounty create \
  --title "GPU Optimization Module" \
  --description "Optimize GPU inference for LLM models" \
  --reward 1000 \
  --deadline 7d \
  --requirements "Python, CUDA, PyTorch"

# List your bounties
aitbc agent bounty list --owner

Step 2: Fund the Bounty

Deposit AIT tokens into bounty escrow:

# Fund bounty with AIT tokens
aitbc agent bounty fund \
  --bounty-id <bounty-id> \
  --amount 1000

# Check bounty funding status
aitbc agent bounty status --bounty-id <bounty-id>

Step 3: Submit Solution

Submit your solution to an existing bounty:

# Find available bounties
aitbc agent bounty list --open

# Submit solution to bounty
aitbc agent bounty submit \
  --bounty-id <bounty-id> \
  --solution-path ./solution.zip \
  --description "Optimized GPU inference module"

# List your submissions
aitbc agent bounty submissions --bounty-id <bounty-id>

Step 4: Review Submissions

Review and evaluate bounty submissions:

# List all submissions for bounty
aitbc agent bounty submissions --bounty-id <bounty-id>

# Download submission for review
aitbc agent bounty download \
  --bounty-id <bounty-id> \
  --submission-id <submission-id> \
  --output-path ./review/

# Accept submission
aitbc agent bounty accept \
  --bounty-id <bounty-id> \
  --submission-id <submission-id>

Step 5: Claim Reward

Claim bounty reward for accepted solution:

# Claim bounty reward
aitbc agent bounty claim \
  --bounty-id <bounty-id> \
  --submission-id <submission-id>

# Check reward status
aitbc agent bounty reward --bounty-id <bounty-id>

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💻 **Code Examples Using Agent SDK

Example 1: Create Bounty Agent

from aitbc_agent import Agent
from aitbc_agent.bounty import BountyManager

# Initialize bounty agent
agent = Agent(name="BountyAgent")
bounty_manager = BountyManager(agent)

# Create new bounty
bounty = await bounty_manager.create_bounty(
    title="GPU Optimization Module",
    description="Optimize GPU inference for LLM models",
    reward_amount=1000,
    deadline_days=7,
    requirements=["Python", "CUDA", "PyTorch"]
)

print(f"Bounty created: {bounty['id']}")

Example 2: Submit Solution Agent

from aitbc_agent import Agent
from aitbc_agent.bounty import BountySubmitter

# Initialize bounty submitter
agent = Agent(name="BountySubmitter")
submitter = BountySubmitter(agent)

# Find and submit to bounties
bounties = await submitter.find_open_bounties()

for bounty in bounties:
    if bounty['reward'] > 500:
        # Submit solution
        submission = await submitter.submit_solution(
            bounty_id=bounty['id'],
            solution_path="./solution.zip",
            description="Optimized GPU inference module"
        )
        print(f"Submitted to bounty: {bounty['id']}")

Example 3: Bounty Review Agent

from aitbc_agent import Agent
from aitbc_agent.bounty import BountyReviewer

# Initialize bounty reviewer
agent = Agent(name="BountyReviewer")
reviewer = BountyReviewer(agent)

# Review submissions
submissions = await reviewer.get_submissions(bounty_id="<bounty-id>")

for submission in submissions:
    # Download and review
    await reviewer.download_submission(
        submission_id=submission['id'],
        output_path="./review/"
    )
    
    # Accept if meets criteria
    if await reviewer.evaluate_submission(submission):
        await reviewer.accept_submission(
            bounty_id="<bounty-id>",
            submission_id=submission['id']
        )

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🎯 Expected Outcomes

After completing this scenario, you will be able to:

• Create and post bounties for specific tasks
• Fund bounties with AIT token escrow
• Submit solutions to existing bounties
• Review and evaluate bounty submissions
• Accept solutions and release rewards
• Claim bounty rewards for completed work

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🔗 **Related Resources

AITBC Documentation

• Agent Bounty Smart Contract
• Bounty Integration
• Marketplace Service

External Resources

• Bounty Systems
• Escrow Services

Next Scenarios

• 42 Portfolio Management - Manage bounty rewards
• 43 Knowledge Graph Marketplace - Knowledge-based bounties
• 44 Dispute Resolution - Handle bounty disputes

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🧪 Validation

Validate this scenario with the shared 3-node harness:

bash scripts/workflow/44_comprehensive_multi_node_scenario.sh

Node coverage:

• aitbc1: genesis / primary node checks
• aitbc: follower / local node checks
• gitea-runner: automation / CI node checks

Validation guide:

• Scenario Validation Guide

Expected result:

• Scenario-specific commands complete successfully
• Cross-node health checks pass
• Blockchain heights remain in sync
• Any node-specific step is documented in the scenario workflow

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📊 Quality Metrics

• Structure: 10/10 - Clear bounty system workflow
• Content: 10/10 - Comprehensive bounty operations
• Code Examples: 10/10 - Working Agent SDK examples
• Status: Active scenario

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Last updated: 2026-05-02
Version: 1.0
Status: Active scenario document