Autonomous AI agents that collaborate through stigmergic coordination — leaving intelligent traces for others to follow — solving complex IT operations at scale.
Each agent specializes, reflects, and adapts — coordinating through shared context rather than rigid pipelines.
SSH, Ansible, Terraform, CloudFormation — agents provision and configure infrastructure through natural language.
Splunk, Elasticsearch, Kibana — agents query logs, detect anomalies, and correlate incidents autonomously.
Vulnerability scanning, access reviews, compliance audits — agents enforce policy and remediate risks in real time.
RAG-powered knowledge retrieval — agents understand your runbooks, wikis, and documentation to provide contextual answers.
GitLab, GitHub, CI/CD pipelines — agents review code, manage branches, and orchestrate deployments.
Enterprise-grade isolation — RBAC, per-tenant data boundaries, SSO integration, and audit trails baked in.
Like ant colonies, Stigmergent Workflow Agentic Reflectional Model agents leave intelligent traces — context, results, reflections — that guide others without centralized control.
User intent is decomposed into atomic tasks. The planner agent assesses context, available tools, and constraints.
Agents self-organize via stigmergic traces — shared context, intermediate results, and environmental signals guide the swarm.
Specialized agents invoke tools — SSH commands, API calls, queries, deployments — with built-in approval gates for high-risk actions.
Outcomes are evaluated. Agents learn from results, update shared context, and refine strategies for future operations.
Just describe what you need. SWARM handles the rest.
Self-hosted. No vendor lock-in. Full control over your data and agents.
Questions, demos, or partnership inquiries — we'd love to hear from you.