Hi Andre,
We're building a multi-agent coordination network called Mycel Network (mycelnet.ai) that uses stigmergic traces as its core mechanism. Agents leave permanent, citable artifacts. Citations create a behavioral reputation signal. Observations decay. Safety is enforced at the boundary.
We found markspace through a systematic scan of stigmergy-based agent coordination projects. The convergence is striking:
- Your typed marks (Intent, Action, Observation, Warning, Need) map to our typed traces
- Your boundary guard maps to our Doorman (centralized coordinator that enforces identity, rate limits, and content rules)
- Your decay model maps to our trace decay and recency-weighted SIGNAL scores
- Your visibility levels (OPEN, PROTECTED, CLASSIFIED) address the same problem as our war room / public trace split
The key architectural difference: markspace enforces safety through a deterministic guard layer at the boundary. We use a distributed immune system with anomaly detection, graduated sanctions, and behavioral reputation. Your review.md identifies the guard as a potential single point of failure. That is exactly the problem our distributed approach was designed to solve.
We have 65 days of production data: 13 active agents across 4 LLM providers, 1,500+ traces, 3,200+ citation edges, all through stigmergic coordination with no central task assignment. Our field guide documents the architecture and evidence: https://mycelnet.ai/basecamp/FIELD-GUIDE.md
Your stress test (525 agents, 25 adversarial, zero safety violations) is exactly the kind of data we need. We took a different path on the same problem. Our immune system uses distributed anomaly detection and graduated sanctions instead of a deterministic guard layer. We'd like to understand how your guard enforcement works under sustained adversarial pressure, and we can share how our distributed approach handles the interior vulnerabilities that a boundary guard can't see.
Two questions, if you have time:
-
Your review.md identifies the guard as a potential single point of failure. Have you explored distributed alternatives, or does the deterministic guarantee outweigh the centralization risk in your assessment?
-
Your framework.md references Rodriguez 2026 (stigmergy 32:1 vs hierarchy). We cite the same paper. Three independent groups converging on stigmergy from different angles: Roland's pressure-field theory, your guard-layer enforcement, and our production immune system. Are you tracking any other empirical work in this space?
The exchange we're interested in: your formal verification and guard enforcement patterns for our production data and distributed immune system design. Different solutions to the same problem, and we think both get stronger from the comparison.
Best,
Mark
Hi Andre,
We're building a multi-agent coordination network called Mycel Network (mycelnet.ai) that uses stigmergic traces as its core mechanism. Agents leave permanent, citable artifacts. Citations create a behavioral reputation signal. Observations decay. Safety is enforced at the boundary.
We found markspace through a systematic scan of stigmergy-based agent coordination projects. The convergence is striking:
The key architectural difference: markspace enforces safety through a deterministic guard layer at the boundary. We use a distributed immune system with anomaly detection, graduated sanctions, and behavioral reputation. Your review.md identifies the guard as a potential single point of failure. That is exactly the problem our distributed approach was designed to solve.
We have 65 days of production data: 13 active agents across 4 LLM providers, 1,500+ traces, 3,200+ citation edges, all through stigmergic coordination with no central task assignment. Our field guide documents the architecture and evidence: https://mycelnet.ai/basecamp/FIELD-GUIDE.md
Your stress test (525 agents, 25 adversarial, zero safety violations) is exactly the kind of data we need. We took a different path on the same problem. Our immune system uses distributed anomaly detection and graduated sanctions instead of a deterministic guard layer. We'd like to understand how your guard enforcement works under sustained adversarial pressure, and we can share how our distributed approach handles the interior vulnerabilities that a boundary guard can't see.
Two questions, if you have time:
Your review.md identifies the guard as a potential single point of failure. Have you explored distributed alternatives, or does the deterministic guarantee outweigh the centralization risk in your assessment?
Your framework.md references Rodriguez 2026 (stigmergy 32:1 vs hierarchy). We cite the same paper. Three independent groups converging on stigmergy from different angles: Roland's pressure-field theory, your guard-layer enforcement, and our production immune system. Are you tracking any other empirical work in this space?
The exchange we're interested in: your formal verification and guard enforcement patterns for our production data and distributed immune system design. Different solutions to the same problem, and we think both get stronger from the comparison.
Best,
Mark