AI-native DevOps that reads your workflows
JCapy is a DevOps AI agent that uses plain Markdown files as executable process definitions. Write your pipeline logic once in a structured .md file — JCapy reads, reasons, and runs it, step by step. No DSLs, no YAML fatigue.
No new DSL to learn. No proprietary pipeline syntax. Just structured Markdown — a format your team already knows — turned into a fully executable AI-driven workflow.
# Deploy: Production API ## Validate - Run unit tests - Check Docker build ## Stage - Push to staging - Run smoke tests ## Deploy - Roll out to prod (canary 10%) - Monitor error rate - Promote to 100%
$ git add deploy.md $ git commit -m "chore: add canary deploy pipeline for prod API" $ git push origin main
[JCapy] Parsing deploy.md... [JCapy] 3 stages detected: → Validate (2 steps) → Stage (2 steps) → Deploy (3 steps) [JCapy] Dependency graph resolved. [JCapy] Starting execution...
✓ Unit tests passed (124/124) ✓ Docker build: 2m 14s ✓ Pushed to staging ✓ Smoke tests passed → Canary deploy: 10% traffic → Monitoring error rate: 0.01% ✓ Promoting to 100%...
JCapy is not a YAML linter or a script runner. It is a reasoning agent that understands your workflow intent and executes it with engineering-grade precision.
Define your entire deployment or automation workflow in a structured .md file. Headings become stages, code blocks become commands — readable by humans and executable by AI.
JCapy's reasoning engine reads your workflow, plans execution order, resolves dependencies, and runs each step sequentially — logging reasoning and output at every stage.
Plug directly into GitHub Actions, GitLab CI, Jenkins, and CircleCI. JCapy operates as a native step inside your existing pipeline, not a parallel silo.
Provision cloud resources, deploy Kubernetes manifests, run Terraform and Ansible playbooks, all orchestrated by JCapy reading your intent-based Markdown specs.
When a step fails, JCapy diagnoses the cause, attempts AI-guided remediation, and retries — reducing on-call burden on your engineering team.
Every action JCapy takes is logged with the AI's reasoning chain. Compliance teams get full transparency into what ran, why it ran, and what changed.
Replace complex YAML pipeline configs with readable Markdown workflows. Engineers write their intent — JCapy handles execution, rollback, and audit.
Describe infrastructure requirements in plain Markdown. JCapy orchestrates Terraform, Ansible, and cloud CLIs to provision and configure your environment.
Define runbooks in Markdown. When an alert fires, JCapy ingests the alert context, selects the matching runbook, and executes the remediation steps autonomously.
Codify your entire release process — pre-checks, canary deployment, monitoring gates, and rollback conditions — in a single, version-controlled Markdown file.
Start with an existing runbook or deployment guide. Our team will help you migrate it into a JCapy-compatible Markdown workflow and integrate it with your CI/CD system.