AI agents handle every stage — from signals to deploy. Humans stay in control. Every action is audited.
From interactive sessions to automated workflows — the building blocks that power every stage of the lifecycle.
Interactive and non-interactive sessions with any AI agent in isolated containers
Multi-step pipelines that chain agents, tools, and human review
Run any Docker-based tool with parameters, files, and secrets
Real-time usage tracking with per-session, per-user, per-project breakdown
Auto-inject MCPs, skills, repos, and secrets into every session
Every artifact carries full provenance — which workflow, step, agent, and session created it. Define once, run continuously.
Three layers working in concert — humans set direction, AI agents execute each stage, and every action flows to an immutable audit core.
Connect your entire dev stack. Version control, CI/CD, issue trackers, chat, MCP servers — Palad plugs in everywhere.
Agents create branches, push code, and open pull requests directly in your repos. Review AI work the same way you review human work.
Read the docs →Assign tickets to agents and track progress alongside your team's work. Automatic status updates as workflows complete.
Read the docs →Trigger workflows from any CI system. Automate refactors, dependency updates, code reviews, and codebase maintenance at scale.
Read the docs →Connect any MCP server — databases, documentation, custom APIs, internal tools. Agents get the context they need automatically.
Read the docs →Get notified when workflows complete, sessions need attention, or budgets approach limits. Delegate tasks from chat in plain English.
Read the docs →Invite your team, set permissions, and enforce policies. Enterprise plans include single sign-on, provisioning, and org-level controls.
Read the docs →Anthropic Claude, OpenAI Codex, Google Gemini, and any OpenAI-compatible endpoint. Assign the right model to each SDLC stage — use Claude for planning, Codex for code generation, Gemini for review.
Read the docs →Docker, Kubernetes, or your custom runtime. Every agent session runs in complete isolation. Scale horizontally with your existing infrastructure.
Read the docs →Export metrics to Datadog, Grafana, or Prometheus. Track agent performance, workflow durations, error rates, and cost efficiency across all SDLC stages.
Read the docs →Integrate Snyk, SonarQube, or Semgrep as workflow steps. Agents automatically run security scans, flag vulnerabilities, and enforce coding standards before merge.
Read the docs →Palad brings the missing layer between humans and AI agents — governance, traceability, and control at every stage of your SDLC.
Every agent action, every workflow step, every artifact — logged with full provenance. Governance, compliance, and explainability built into the platform from day one.
Humans set direction, workflows orchestrate the stages, AI agents execute in isolated containers. Clear separation of control, logic, and execution.
Claude for planning, Codex for generation, Gemini for review — chain different agents across SDLC stages in a single workflow.
Signals, Triage, Plan, Build, Test, Review, Deploy, Monitor — agents participate at every stage, not just code generation.
Humans approve at key gates — triage decisions, code reviews, deploy sign-offs. AI proposes, humans dispose.
Per-session, per-agent, per-stage cost tracking. Know exactly what each SDLC stage costs and optimize accordingly.
Every agent session runs in its own Docker or K8s container. No shared state, no credential leaks, no cross-contamination.
Every artifact carries its lineage — which signal triggered it, which agent built it, who approved it. From ticket to production.
Pre-built and customizable workflow templates for common SDLC patterns. Start in minutes, tailor to your team's process over time.
Connect to your existing stack — Git, CI/CD, monitoring, ticketing systems. Agents work with your tools, not instead of them.
Palad is designed to meet the demands of modern engineering teams — secure, scalable, and ready to integrate with your existing tools.
Industry-grade security and compliance. Every session runs in complete isolation — no shared state, no credential leaks. Secrets are encrypted at rest and scoped to individual containers.
Learn more about securityWorks with any AI model provider, any IDE, and any container runtime. Swap agents, models, or tools without changing your workflow. As your tooling matures, so does Palad.
Learn more about enterpriseLet AI agents handle every stage — with full traceability.