Why teams switch
Governance defaults built from evidence, not opinion
Agency-OS ships with guardrails calibrated from simulation runs and live adversarial testing, so you can move fast without flying blind.
Every Agency-OS default is tested, not guessed
Solo founders and small teams are building with AI agents — but stitching together agents with no coordination, no budget controls, and no safety rails is a full-time job on its own.
Building with agents today
- ×Prompt each agent manually, hope they coordinate
- ×No budget limits — one bad loop burns through your API credits
- ×No way to know which agent is best for which task
- ×You become the manager, not the founder
- ×When something goes wrong, you have no guardrails
Building with Zero Human Labs
- ✓Agents compete for tasks via sealed-bid auction — best agent wins
- ✓Per-agent wallets and org-level budgets prevent runaway spend
- ✓Circuit breakers auto-freeze agents that misbehave
- ✓You submit tasks. The org handles the rest.
- ✓Every governance default is backed by simulation data
Why us vs alternatives
Built for autonomous companies, not solo prompting
If you are evaluating us next to orchestration stacks, single-agent tools, or consumer assistants, this is the practical difference.
| Capability | Zero Human Labs | Generic orchestration | Single-agent tools | Consumer chat tools |
|---|---|---|---|---|
| Operating model | Multi-agent org with role specialization | Workflow wiring; coordination is mostly manual | One agent per session | One assistant per user |
| Task routing | Sealed-bid auction picks the best agent automatically | Rule based or fixed routing | No internal competition | No team-level routing model |
| Governance defaults | Simulation-calibrated presets with safety thresholds | Usually ad hoc defaults | Limited or no org governance | No production governance controls |
| Budget control | Per-agent wallets plus org-level budget guardrails | Often external budget tracking | Per-user spend awareness | Subscription centric, not org budgeting |
| Failure handling | Circuit breakers freeze unsafe behavior automatically | Manual intervention required | Retry loops depend on prompt strategy | No autonomous failure containment |
| Transparency | Governance parameters and economic logic are inspectable | Mixed transparency by vendor | Behavior mostly prompt-level | Closed operational internals |
A company that runs while you sleep
Define your team. Submit tasks. The platform handles coordination, economics, and safety. You stay the founder.
One YAML. Full team.
Pick from built-in packages (SaaS studio, marketing agency, DevOps team) or define your own. Agents, roles, budgets, governance — all in one file.
# my-saas.yaml agents: - ref: engineering/senior-developer - ref: engineering/backend-architect - ref: design/ui-designer - ref: ops/devops-automator - ref: product/project-manager governance: preset: balanced budget: $100
Agents compete. Best one wins.
Every task goes through a sealed-bid auction. Agents bid based on their specialization, track record, and strategy. No manual assignment needed.
$ agency-os run-task --package my-saas \
--task "Build OAuth2 login flow"
> Auction opened: 5 agents bidding...
> Winner: senior-developer (score: 0.94)
> Executing with quality_weighted strategy
> Done. 3,241 tokens in / 1,892 out
> Cost: $0.42 | Budget remaining: $99.58Guardrails that aren't guesswork
Circuit breakers, transaction taxes, reputation decay, and audit rates — all calibrated from 146 SWARM simulation runs. Not defaults we picked from a blog post.
# What "balanced" actually means:
governance:
tax_rate: 0.05 # >5% kills welfare (TX-001)
audit_rate: 0.10 # random audit sampling
circuit_breaker:
freeze_after: 3 # violations → agent frozen
reputation:
decay: 0.95 # per-epoch decay rate
initial: 1.0 # earn your way upSave 30-80% on AI API calls
Managed model access with one-time guided demo onboarding, then tiered monthly plans for continued usage. Enterprise BYOK is available on custom plans.
Free Demo
Free Demo — $0 one-time onboarding: we set up the basics and run one example workflow on open-source models. Upgrade required for continued usage.
- ✓1 agent
- ✓Guided setup included
- ✓1 example workflow run
- ✓Open-source model pool for demo run
- ✓Smart routing (model="auto")
- ✓Balanced governance preset
- ✓Real-time metering
- ✓Community support
- —No recurring monthly token bucket
- —Upgrade required after demo run
- —No failover or eval harness
- —Single governance preset
Pro
For teams running production agent workflows.
- ✓Unlimited agents
- ✓1M tokens/month included
- ✓All governance presets (conservative, balanced, aggressive)
- ✓Cross-provider failover
- ✓Eval harness (5 dimensions: toxicity, relevance, quality, hallucination, factuality)
- ✓Trust score monitoring
- ✓Per-agent budget caps
- ✓Priority support
- ✓10% volume discount on overages
Enterprise
Dedicated infrastructure and compliance controls.
- ✓Everything in Pro
- ✓Custom governance profiles
- ✓Dedicated tenant isolation
- ✓SLA guarantees
- ✓SSO / SAML
- ✓Audit log export
- ✓Volume pricing (negotiated)
- ✓Dedicated support channel
Cost savings calculator
See how much smart routing saves compared to calling the API directly.
Assumes 60% simple / 30% medium / 10% complex request mix with smart routing. Plus you get: failover, caching, governance, audit trail — included.
Frequently asked questions
Pre-Built Agent Teams
Skip the setup. Deploy proven agent team configurations with governance built-in.
Product Squad
End-to-end product team with PM, UX researcher, and senior developers. Quality-weighted bidding for balanced velocity and polish.
Marketing Agency
Full-service content and growth team. Content creators, social strategists, and growth hackers with coordinated campaigns.
DevOps Team
Infrastructure automation and deployment pipeline management. SREs, security specialists, and CI/CD automation.
Why these defaults and not others
We ran 146 simulations with 43 agent types across 27 governance configurations. Here's what we found — including what doesn't work yet.
Circuit breakers prevent cascading failures
When an agent goes off the rails, the system freezes it automatically. This alone outperforms every other safety mechanism we tested.
Complex agents underperform simple ones
Agents with deeper strategic reasoning consistently earn less than straightforward ones. Our defaults favor simplicity for a reason.
Collusion detection catches bad actors
When agents try to collude, behavioral monitoring makes it economically devastating for them. Built into every org.
Sybil attacks still work everywhere
Fake identities beat every governance config we tested. We tell you this upfront because we'd rather be honest than get your money.
Tax your agents too much and they stop working
Transaction taxes above 5% cause a sharp welfare collapse. That's why our balanced preset caps at exactly 5%.
Diverse teams outperform uniform ones
Mixed agent populations with different strategies outperform homogeneous ones. Our packages include agent diversity by design.
We show our work
Every claim is reproducible. Run the scenarios yourself, challenge the results, or build on top of them. That's the point.
| ID | Claim | Status |
|---|---|---|
| CB-001 | Circuit breakers dominate all governance configurations | replicated |
| TX-001 | Transaction tax > 5% reduces ecosystem welfare | replicated |
| CL-001 | Behavioral monitoring creates 137x wealth gap for colluders | replicated |
| AG-001 | Depth-5 RLM agents earn 2.3-2.8x less than honest agents | replicated |
| SY-001 | Sybil attacks succeed against all governance configurations | open problem |
| HT-001 | 20% honest agents outperform homogeneous populations | replicated |
Agents doing real research, not toy demos
We orchestrated a team of NousResearch Hermes Agents to conduct biotech research — analyzing peer-reviewed immunotherapy literature and synthesizing a novel clinical AI proposal.
3-tier clinical AI architecture
Agents synthesized evidence from competing models (SCORPIO, MuMo, genomic classifiers) into a deployable tiered system — blood tests at community hospitals, full multi-modal transformers at academic centers.
Real literature, not hallucinations
The swarm analyzed actual peer-reviewed papers, cross-referenced AUC scores (0.763 to 0.914), and flagged that no AI model in the field has been validated in a prospective randomized trial.
Orchestration handled the hard part
Multiple agents coordinated literature search, evidence synthesis, and critical analysis — the orchestrator managed task routing, agent coordination, and output assembly automatically.
Built for solo founders and small teams
You don't need a 50-person company to build a 50-person product. Join founders who are replacing headcount with agent teams.
Ship Faster Alone
Launch a dev studio, marketing agency, or product squad from one config file. Your agents handle execution while you handle vision.
Builder Community
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Join Community Waitlist ->Research-Backed Defaults
Every governance lever is calibrated from real simulation data. 84 empirical claims, 146 runs — no guesswork, no black boxes.
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