Build the real-time collaborative editor foundation with CRDT-based synchronization supporting multiple simultaneous cursors from human developers and AI agents. Implement the Rholang channel-based agent communication protocol — the infrastructure through which AI agents coordinate by sending and receiving messages on Rholang channels, dogfooding ASI:Chain's concurrency model for the IDE itself. Develop the first two AI agents: the Rholang Architect agent (process topology design, channel allocation, process composition suggestions) and the Verification Agent (continuous MeTTa type checking via Hyperon inference, type annotation generation, inline type violation flagging). Each agent appears as a live cursor in the editor with distinct color and icon. Build the collaborative interface layer: inline agent suggestions with accept/reject controls, collaboration timeline showing all human and agent edits with replay capability, and the agent communication panel displaying Rholang channel messages between agents with readable explanations of inter-agent reasoning. Establish the shared Atomspace context that all agents read from and write to, enabling cross-agent knowledge sharing.

1. CRDT-based collaborative editor with real-time synchronization supporting 5+ simultaneous cursors (human and AI agents) with sub-200ms sync latency 2. Rholang channel-based agent communication protocol: agents send structured messages through typed Rholang channels, with protocol specification published as open standard 3. Rholang Architect agent: analyzes contract intent, suggests process decompositions, inserts channel declarations, arranges process compositions with live cursor visualization 4. Verification Agent: continuous MeTTa type checking via Hyperon inference, automatic type annotation generation, inline violation flagging within 3 seconds of any edit 5. Collaborative interface: inline suggestions with accept/reject, collaboration timeline with replay, agent communication panel showing Rholang channel messages 6. Shared Atomspace context: agents write analysis results as MeTTa atoms, queryable by other agents and by the human developer 7. Three example collaboration scenarios demonstrating human-agent workflow for token, escrow, and governance contracts

$19,000 USD

1. Collaborative editor maintains consistency with 5 simultaneous cursors — no lost edits, no ordering conflicts, sync latency under 200ms measured across 100 concurrent edit sequences 2. Agent communication via Rholang channels handles message exchange between Architect and Verification agents with under 500ms round-trip latency 3. Rholang Architect agent produces valid process topology suggestions for 4 of 5 test contract scenarios — suggestions compile without errors 4. Verification Agent detects and flags type violations within 3 seconds of the causing edit, with zero false negatives across a test suite of 30 known type errors 5. Collaboration timeline accurately records and replays all human and agent edits in correct chronological order 6. Agent communication panel displays readable Rholang channel messages with natural language explanations of agent reasoning 7. All code published to public GitHub repository under MIT license

Develop the remaining two AI agents: the Security Auditor agent (continuous vulnerability probing with concrete attack scenario construction) and the DevNet Tester agent (automatic deployment and testing on DevNet with BlockDAG behavior monitoring). The Security Auditor uses pattern matching against known vulnerability classes plus Atomspace-based formal reasoning to discover novel attack vectors specific to each contract's process topology, constructing proof-of-concept attack sequences that demonstrate exploitability on ASI:Chain's BlockDAG. The DevNet Tester continuously deploys compilable snapshots, runs regression tests, explores edge cases, and monitors CBC Casper consensus and BlockDAG ordering. Failed test paths are highlighted directly in the editor. Integrate Singularity Compute to power all four agents as independently scalable services. Implement the trust escalation system where developers gradually grant agents increasing autonomy — from suggestion-only mode to auto-deploy testing and auto-insert annotations. Build conflict resolution for contradictory agent suggestions: present both suggestions with reasoning from each agent's Atomspace analysis, letting the human developer decide. Implement the emergent coordination capabilities enabled by shared Atomspace: Security Auditor leveraging Verification Agent type judgments, Architect using DevNet Tester performance data.

1. Security Auditor agent: continuous vulnerability probing across 10+ vulnerability classes (reentrancy, front-running, channel hijacking, unauthorized access, state manipulation, DoS, integer overflow, race conditions, privilege escalation, replay attacks) with concrete proof-of-concept attack scenarios 2. DevNet Tester agent: automatic deployment of compilable snapshots, regression testing, property-based edge case exploration, BlockDAG behavior monitoring under CBC Casper consensus with transaction ordering analysis 3. Singularity Compute integration: all four agents running as independently scalable services with auditable computation records and per-agent resource scaling 4. Trust escalation system: configurable agent autonomy levels from suggestion-only to auto-deploy and auto-annotate, with per-agent and per-action granularity 5. Conflict resolution interface: contradictory agent suggestions presented side-by-side with Atomspace-derived reasoning from each agent 6. Cross-agent coordination: Security Auditor uses Verification Agent type info, Architect uses DevNet Tester performance data, all through shared Atomspace

$17,000 USD

1. Security Auditor detects injected vulnerabilities in 4 of 5 test contracts and constructs concrete attack scenarios demonstrating exploitability — each scenario includes a message sequence executable on DevNet 2. DevNet Tester successfully auto-deploys contract snapshots within 30 seconds of compilable state, runs regression tests, and reports results inline in the editor 3. BlockDAG monitoring detects and reports behavioral differences across 3+ different transaction ordering scenarios for each deployed contract 4. Singularity Compute runs all four agents concurrently with independent scaling — Auditor deep analysis does not degrade Verification Agent responsiveness 5. Trust escalation correctly restricts agent actions based on configured autonomy level — agents in suggestion-only mode never modify code without explicit approval 6. Cross-agent coordination demo: Security Auditor narrows vulnerability search using Verification Agent type constraints, reducing false positives by at least 30% 7. Conflict resolution correctly identifies and presents contradictory suggestions from two agents in 3 of 3 seeded contradiction scenarios

Build the open agent protocol specification enabling third-party developers to create custom CoForge agents and publish them on the SingularityNET marketplace. The protocol defines how agents connect to the shared Atomspace, communicate through Rholang channels, render suggestions in the collaborative editor, and declare their capabilities and resource requirements for Singularity Compute scaling. Create two example third-party agents as templates: a Gas Optimizer agent (analyzes Rholang process structure for compute cost reduction) and a Documentation agent (generates and maintains inline documentation from Atomspace type information and agent analysis results). These demonstrate the extensibility of CoForge's multi-agent architecture. Conduct user testing with developers of varying experience levels to validate the multiplayer paradigm. Produce comprehensive documentation covering the collaborative workflow, agent capabilities, trust escalation configuration, and the third-party agent development guide. Create video demonstrations showing real-time human-agent collaboration on ASI:Chain contract development from initial design through DevNet deployment.

1. Open agent protocol specification: Atomspace connection, Rholang channel communication, editor rendering API, capability declaration, Singularity Compute resource requirements — published as versioned standard 2. SingularityNET marketplace integration: agents discoverable, installable, and configurable from within CoForge 3. Gas Optimizer template agent: analyzes process structure for compute cost reduction on ASI:Chain with before/after cost comparison 4. Documentation template agent: generates inline docs from Atomspace type information and agent analysis, updates automatically as code evolves 5. User testing with 5+ developers at varying experience levels — structured task completion, trust development tracking, and satisfaction metrics 6. Documentation suite: collaborative workflow guide, agent capabilities reference, trust escalation configuration, third-party agent development tutorial 7. Video demonstrations: two 10-minute walkthroughs showing real-time human-agent collaboration from contract design to DevNet deployment

$12,000 USD

1. Third-party agent protocol enables external developers to build, test, and deploy a custom CoForge agent using only the published specification and documentation — validated by having a developer outside the team create a working agent 2. SingularityNET marketplace lists CoForge agents with install/configure workflow completing in under 5 minutes 3. Gas Optimizer agent identifies at least one compute cost reduction opportunity in 4 of 5 test contracts with measurable cost improvement 4. Documentation agent produces accurate inline documentation that updates within 10 seconds of relevant code changes 5. User testing shows developers successfully complete a contract development task using CoForge's multi-agent collaboration within 2x the time of experienced solo development 6. Documentation enables new users to configure agent trust levels, use the collaboration timeline, and understand agent suggestions without external assistance