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- Update workflow paths from docs/openclaw to docs/hermes - Rename skill prefixes from openclaw-* to hermes-* - Update agent skill references in refactoring and analysis docs - Rename OPENCLAW_AITBC_MASTERY_PLAN.md to reflect hermes branding - Update CLI examples and command references throughout documentation
2.2 KiB
2.2 KiB
Phase 1: Hermes Autonomous Economics
Overview
This phase aims to give Hermes agents complete financial autonomy within the AITBC ecosystem. Currently, users must manually fund and approve GPU rentals. By implementing autonomous agent wallets and bidding strategies, agents can negotiate their own compute power dynamically based on the priority of the task they are given.
Objectives
- Agent Wallet & Micro-Transactions: Equip every Hermes agent profile with a secure, isolated smart contract wallet (
AgentWallet.sol). - Bid-Strategy Engine: Develop Python services that allow agents to assess the current marketplace queue and bid optimally for GPU time.
- Multi-Agent Orchestration: Allow a single user prompt to spin up a "Master Agent" that delegates sub-tasks to "Worker Agents", renting optimal hardware for each specific sub-task.
Implementation Steps
Step 1.1: Smart Contract Upgrades
- Create
AgentWallet.solderived from OpenZeppelin'sERC2771Contextfor meta-transactions. - Allow users to set daily spend limits (allowances) for their agents.
- Update
AIPowerRental.solto accept signatures directly fromAgentWalletcontracts.
Step 2.1: Bid-Strategy Engine (Python)
- Create
src/app/services/agent_bidding_service.py. - Implement a reinforcement learning model (based on our existing
advanced_reinforcement_learning.py) to predict the optimal bid price based on network congestion. - Integrate with the
MarketplaceGPUOptimizerto read real-time queue depths.
Step 3.1: Task Delegation & Orchestration
- Update the
Hermes Enhanced Serviceto parse complex prompts into DAGs (Directed Acyclic Graphs) of sub-tasks. - Implement parallel execution of sub-tasks by spawning multiple containerized agent instances that negotiate independently in the marketplace.
Expected Outcomes
- Agents can run 24/7 without user approval prompts for every transaction.
- 30% reduction in average task completion time due to optimal sub-task hardware routing (e.g., using cheap CPUs for text formatting, expensive GPUs for image generation).
- Higher overall utilization of the AITBC marketplace as agents automatically fill idle compute slots with low-priority background tasks.