Agent workflows that don't break production

Orchestrate multi-step AI agent runs with checkpoints, retries, and human gates—no single-shot failures, no lost state.

Why single-shot agents fall short

Your AI-assisted code generation pipeline isn't a one-liner. It needs staging, approval, rollback, audit. AgentDAG brings release-grade reliability to agent workflows.

Built for your release pipeline

YAML-first orchestration

Declare agent inputs and outputs once. DAG handles state, sequencing, and idempotency across runs.

Automatic retry & rollback

Failed stages retry with exponential backoff. Rollback to last known good state without manual intervention.

Human approval gates

Insert review checkpoints mid-workflow. Agents pause; humans decide; DAG resumes with full context.

GitHub & GitLab native

Trigger DAGs from PRs, branch pushes, or manual webhooks. Results stream back as PR comments and checks.

Real-time agent output

Watch agent reasoning unfold. Streaming logs and structured events let you debug and intervene instantly.

Audit & compliance

Every gate decision, retry, and rollback is logged. Export trails for compliance and post-mortem analysis.

How it works

Define your DAG in YAML. Each stage chains agent calls with input/output contracts. AgentDAG manages retries, gates, and state. Agents focus on reasoning; DAG handles reliability.

See a live walkthrough

Who's building with AgentDAG

Platform teams at mid-market SaaS and fintech companies automating code review, security scanning, and deployment validation with AI agents.

Pricing

Pay per DAG run + per gate approval. Premium tier unlocks custom integrations and on-prem deployment.

What counts as a run?

Each end-to-end DAG execution, including retries within the same logical workflow.

How are gate approvals billed?

One approval per human gate decision. Rejections and re-runs don't incur additional charges.

Request early access