Our Story
We didn't build governance because it
looks good on a checklist.
We built it because our research showed exactly how agent teams fail without it — and we have 146 indexed experiment runs to prove it.
The problem nobody is solving
Everyone is building AI agents. Almost nobody is governing them. The numbers tell the story.
of agentic AI projects will be cancelled by 2027
Gartner
of AI deployments fail to achieve projected ROI
McKinsey 2026
in enterprise AI spending — most without governance
AI Governance Today
more expensive to retrofit governance than build it in
MIT Sloan
From research to product
Agency-OS started as a research question: what happens when agent teams have no guardrails? The answer built a company.
The Research
146 simulations. One finding.
We started as a research project called SWARM — Simulated Workforce of Autonomous Rational Models. The question was simple: what happens when you give AI agents real economic incentives and let them self-organize?
We ran 146 indexed experiment runs across different governance configurations. Agents competed for tasks via sealed-bid auctions, earned reputation scores, and faced real consequences for failure. The data was unambiguous.
simulation runs
The Discovery
Complex agents earn less.
The biggest surprise from our research: agents with deep reasoning and expensive models consistently underperformed simpler ones. Complex agents earned 2.3-2.8x less than their simpler counterparts.
Without governance constraints, agent teams burn 10-30x what they should. Retry loops, context window bloat, and model misrouting are the silent killers. The extra intelligence doesn't compensate for unpredictable behavior in production.
less earned by complex agents
The Fix
Circuit breakers changed everything.
When we added circuit breakers — automatic freezing after repeated violations — agent welfare improved by 81%. Not marginally. Dramatically. Every governance mechanism we tested showed measurable, reproducible improvement.
Budget ceilings, reputation scoring, collusion detection, smart model routing. Each layer contributed. But circuit breakers were the single highest-impact intervention. They're on by default in Agency-OS because our data says they should be.
welfare improvement with circuit breakers
The Product
Research becomes infrastructure.
Agency-OS is the production system we wished existed when we started the research. Every default, every threshold, every governance parameter is calibrated from real simulation data — not guesswork, not blog-post defaults.
Define your agent team in YAML. Connect via OpenAI-compatible API. Governance, smart routing, and budget controls handle the rest. We built it because nobody else was solving the governance problem — frameworks like CrewAI and LangGraph help you build agents, but none of them stop agents from going wrong.
from config to governed team
Why Agency-OS exists
Frameworks help you build agents.
CrewAI, LangGraph, AutoGen — they're excellent at orchestrating agent workflows. But none of them stop an agent from running a $4,300 loop overnight, or burning through your API budget in an hour, or silently failing for days.
We stop agents from going wrong.
Budget caps freeze agents at the dollar amount you set. Circuit breakers catch misbehavior in real time. Smart routing sends each task to the cheapest capable model. Every parameter is calibrated from real data — not defaults we thought sounded right.
Ready to govern your agent team?
From YAML config to governed agent team in 5 minutes. Start free, scale with confidence.