The durable runtime for production AI agents.
Design multi-agent workflows on a canvas, then run them on infrastructure that survives crashes, pauses for human approval, and puts a dollar cost on every step.
- Design multi-agent workflows on a visual canvas
- Every step checkpoints — runs survive crashes and resume
- Human approvals, full audit, and a dollar cost on every step
- Classifyagent
- Draft replyagentqueued
- Fetch contexttoolqueued
- Human reviewgatequeued
- Notify teamtoolqueued
- 10min
- from empty terminal to a governed run
- 100%
- of steps cost-attributed
- 99.9%
- uptime target on Enterprise SLAs
- 2
- SDKs — Python & TypeScript
Built for teams running agents in production
Illustrative placeholders — not current customers
How it works
Whiteboard to production in three moves.
01
Design
Compose agents, tools, conditions, and human gates on the canvas — or in code with the SDK. Graphs are validated before a single token is spent.
canvas · sdk · templates
02
Execute
Runs execute on a durable runtime that checkpoints after every node, with retries, timeouts, and bounded parallelism. Kill the process — the run resumes.
checkpoint · retry · resume
03
Govern
Approvals pause runs durably until a person decides. Every action lands in the audit log, and every step carries a dollar cost finance can read.
approve · audit · attribute
Platform
Everything between a prompt and production.
One platform to design, execute, observe, and govern multi-agent workflows — without stitching together five tools.
Visual workflow builder
Design multi-agent systems on a canvas. Agents, tools, conditions, loops, and gates — each node compiles to real executable graph state.
state persisted after every node.
kill the process — the run continues.
Checkpointed execution
State is persisted after every node. Runs survive crashes and restarts, and resume from the last checkpoint.
Review the drafted refund of $1,240 before it is sent.
Human-in-the-loop
Gate any step behind a structured approval — multi-stage, conditional, with reminder and escalation ladders. The run pauses, durably, until a person decides.
Cost attribution & budgets
Every LLM call is metered — cost per step, per run, per outcome. Cap spend with budgets enforced on every trigger, with atomic reservations and audited overrides.
Audit & policy events
Every change, run, and approval is logged — and every runtime policy decision (denials, redactions, blocks, overrides) streams as an inspectable event. Compliance is built in, not bolted on.
claude-sonnet-4-6
primary
claude-haiku-4-5
fallback 1
gpt-4o-mini
fallback 2
Multi-LLM routing
Anthropic and OpenAI behind one interface, with per-node model selection and governed fallback chains — failover only for approved error classes, never to a model your policy forbids.
Connect anything
MCP in both directions, webhooks, a full REST API, and typed SDKs. If it speaks HTTP, it plugs into the graph.
Reliability
Works in the demo.
And in production.
Most agent projects die at the same wall: they work once, then break under real load. Ballast is built for the second week, not the first demo.
Automatic retries
Transient failures are retried with exponential backoff — per node, with configurable limits.
Per-step timeouts
No agent hangs a workflow. Every node has a hard timeout and a graceful failure path.
Resume from any checkpoint
Kill the process mid-run. State is already in the database, and the run picks up exactly where it stopped.
We built Ballast to replace a pile of cron jobs and retry logic with one governed engine — so runs survive your deploys, refunds wait for a human, and finance finally sees a dollar cost per outcome.
The Ballast team
Why we built it
Observability
See every step. Cost every call.
Full trace of every run — model calls, tool invocations, approvals — with duration and dollar cost attributed per step. Debugging a multi-agent failure stops being archaeology.
classify_ticket
agent
fetch_context
tool
draft_reply
agent
human_review
gate
notify_team
tool
For engineers
Operators get a canvas.
You get an API.
Everything on the canvas is a typed SDK call away. Trigger governed runs from your product, resolve approval gates programmatically, and register your own agents as tools.
- Python & TypeScript SDKs, one-for-one with the REST API
- Idempotent triggers, run polling, and typed errors built in
- MCP in both directions — expose workflows as tools, or call external servers mid-run
from agentos_sdk import AgentOS client = AgentOS("https://api.ballastos.com", api_key="aos_...") run = client.run( "Support Triage Crew", {"ticket": "I was double charged"}, wait=True, # poll until terminal idempotency_key="ticket-8841", # safe to retry) if run["status"] == "paused": # held at a human gate client.approve(run["id"], input={"reviewer": "lukas"}) print(run["status"], run["output"])Templates
Production patterns, one click away.
Start from a working workflow instead of a blank canvas. Every template runs end-to-end the moment you import it.
Enterprise-ready
Governance your security team signs off on.
Audit, access control, and human approval are core engine features — not an enterprise upsell bolted on later. The controls regulated teams ask for are here on day one.
SOC 2 Type II
Controls mapped to SOC 2. The Type II audit is planned — on our roadmap, not yet started.
SSO (OIDC)
Bring Okta, Entra, or Google. Just-in-time provisioning with enforced default roles.
SCIM 2.0
Automatic user provisioning and deprovisioning driven by your identity provider.
MFA & lockout
TOTP two-factor for every member, with brute-force login lockout on by default.
Role-based access
Ten granular capabilities with per-member overrides and role-scoped API keys.
Immutable audit log
Every create, run, approval, and rejection recorded — exportable as CSV.
Encryption & isolation
TLS in transit, secrets encrypted at rest, strict per-workspace data isolation.
Self-host / VPC
Run the same container in your own cloud — no run data leaves your perimeter.
Pricing
Pay for runs, not seats.
Usage-based pricing that scales with your agents. Hard caps by default — no surprise bills.
Free
$0forever
For prototypes and solo builders.
- 1,000 runs / month
- 5 workflows
- Visual builder
- 7-day trace retention
- Community support
Starter
$49/month
For small teams shipping their first production agents.
- 10,000 runs / month
- Unlimited workflows
- Checkpoint resume
- 30-day trace retention
- Email support
Pro
$299/month
For teams running agent fleets that matter.
- 200,000 runs / month
- Governance & audit log
- Human approval gates
- 90-day trace retention
- Team collaboration
- Priority support
Enterprise
Custom
For regulated industries and large orgs.
- Custom volume & SLAs
- SSO (OIDC) & SCIM
- IP allowlist
- Unlimited audit retention
- Self-host option
- Dedicated support
LLM provider costs pass through at cost on all paid tiers. Overage is cap-and-notify by default.
FAQ
Questions, answered.
Start free — no API keys
Ship agents you can trust in production.
Design your first checkpointed workflow, run it on the mock model, and watch every step and its cost — in under thirty minutes.