AUTONOMA
Build a Company.
Hire Zero People.
Zero-employee AI business orchestration — deploy entire companies
in days, not months, powered by autonomous agents.
25
Agents Deployed
4
Live Companies
$12K
MRR
0
Employees
HACKATHON
DEMO 2026
AUTONOMA
THE PROBLEM
Starting a Business Is Broken
New businesses face impossible tradeoffs: expensive talent, slow hiring,
and coordination overhead that kills momentum before revenue arrives.
🕐
3–6 Months
To hire a foundational team. By the time you're staffed, competitors have shipped.
💸
$80K–120K
Per employee per year — before benefits, taxes, or management overhead.
🔀
Coordination Tax
Every new hire adds meetings, bottlenecks, and alignment costs.
📉
60–70% Margins
The ceiling for human-staffed SMBs — capped by headcount costs.
33 million SMBs in the US alone need a better way to build and operate.
THE SOLUTION
Autonoma: The Company OS for AI Agents
Replace your entire org chart with autonomous agents — sales, ops, marketing, finance — running 24/7 at a fraction of human cost.
01
MAP
Analyze your business model. Design the agent org chart — roles, reporting lines, budgets.
02
DEPLOY
Configure agents with goals, skills & schedules. Autonoma control plane goes live in 3–5 days.
03
OPERATE
Agents run on heartbeats & triggers. They delegate, report, stay on budget, escalate only when needed.
"If Claude Code is an employee, Autonoma is the company."
PRODUCT
Agency Creator & Company Instances
Agency Creator
Smart company setup — two modes:
LLM-Guided Mode
Describe your business in plain language. An LLM layer (Gemini + Boundary ML) converts unstructured intent into a structured org chart in one shot.
Prompted Steps
Step-by-step manual setup for users who want precise control over agent roles, budgets, and hierarchy.
File Bootstrap
Upload CSDs, PDFs, or company data — Autonoma extracts structure and prefills your agent config automatically.
Company Instances
Each deployment is a fully isolated company:
Org Chart Engine
Hierarchies with real reporting lines. Every agent has a title, boss, and job description.
Budget Enforcement
Atomic checkout + spend tracking per agent. Automatic stop at budget limits.
Governance Layer
Human approval gates, config versioning, rollback. The board stays in control.
Multi-Company Isolation
One deployment, many companies — complete data separation between instances.
New businesses can bootstrap an entire operating company in under a week — no hiring required.
FEATURES
Agentic Infrastructure — What Sets Us Apart
🤖 Multi-Provider AI
Specify different models per agent. Gemini default, Featherless AI smart routing, Claude, GPT-4, Codex, Cursor — best model for each task.
🔒 Human-in-the-Loop
Optional approval gates on any agent, automation, or heartbeat. Humans stay in control where it matters.
🧵 Multithreading Sync
Barriers, locks & lock-free data structures synchronize agent execution. No race conditions in your org.
🧠 Smart Context Windows
Optimal context allocation prevents sub-agents losing task context during hierarchical delegation.
💰 Cost-Optimal Orgs
For a given dollar budget, Autonoma optimizes org structure, task delegation, and model selection.
📊 Dynamic Model Scoping
Agents can dynamically switch models within defined permission tiers — power where needed, efficiency everywhere else.
🏗️ Obsidian + Nexus
Boost agency context with dependency graphs and knowledge graphs. Agents understand your company's full structure.
🖼️ AutoHDR Ad Generation
Real-estate instances auto-generate high-quality HDR listing images. Purpose-built for property agencies.
🎨 Variant-Powered UI
Premium frontend built with Variant design system — intuitive, modern, and built for non-technical founders.
COST INTELLIGENCE
Smart Job Cost Analysis
Before a task runs, Autonoma analyzes it — scoring complexity, expected token count, and delegation depth — to pick the cheapest model that will actually succeed.
📋
Task
Ingested
Job arrives via heartbeat or delegation trigger
🔬
Complexity
Scoring
LLM scores task on 5 dimensions:
reasoning depth, output length,
tool use, ambiguity, domain spec.
💰
Cost
Estimation
Expected token budget calculated per available model × provider pricing
🎯
Model
Selection
Cheapest model whose capability score meets or exceeds task requirements is selected
🚀
Dispatch
Task dispatched with allocated token budget and model binding
Simple tasks (format, summarize, classify) → Gemini Flash or cheapest open-weight
Medium tasks (draft, analyze, extract) → mid-tier model, token budget capped
Complex tasks (reason, plan, multi-step) → frontier model, uncapped but monitored
Sub-agent delegation inherits parent's cost tier unless explicitly escalated
MODEL ROUTING
Minimum Cost Model Usage + Failover
Agents always run on the cheapest capable model. When a provider runs out of credits, we route to the next optimal — zero downtime, no manual intervention.
Provider Failover Chain
Gemini Flash
~$0.00015 / 1K tok
DEFAULT
Cheapest anchor — used for all simple + medium tasks
Featherless Router
~$0.0004 / 1K tok
FALLBACK 1
Smart meta-router across open-weight models
Claude Haiku
~$0.0008 / 1K tok
FALLBACK 2
Anthropic's efficient tier — strong on reasoning tasks
GPT-4o Mini
~$0.0015 / 1K tok
FALLBACK 3
OpenAI efficient tier — last resort before frontier
Failover triggers: credits < threshold · rate limit error · provider timeout · model deprecation
How It Saves Money
Task-Tier Matching
Simple ops never touch frontier models. A formatting task costs 100× less than running it on GPT-4o.
Budget Binding
Each task dispatched with a token cap. Agents can't over-spend even on complex jobs.
Real-Time Credit Tracking
Per-provider credit balances monitored live. Near-zero balance triggers pre-emptive failover before errors occur.
Zero Downtime Routing
Sub-second re-dispatch to next provider. Agent orgs never stall waiting for a model to come back online.
Featherless as Safety Net
When all direct provider credits are exhausted, Featherless routes to the best available open-weight model — org keeps running at minimal cost.
TECHNOLOGY
Built for Production Scale
Backend
Express.js — REST API + WebSocket server
Drizzle ORM + PostgreSQL — type-safe data layer
Multithreaded sync — barriers, locks, lock-free structs
Docker — self-hosted or cloud deploy
Heartbeat Scheduler — cron + trigger-based agent wakes
Frontend
React 19 + TypeScript + Vite
TailwindCSS — utility-first styling
Variant design system — production-grade UI
WebSockets — live agent activity feed
better-auth — secure authentication
AI / Agents
Gemini (default provider)
Featherless AI — smart model router
Claude Code, Codex, Cursor, GPT-4
Boundary ML — unstructured→structured LLM
AutoHDR — real-estate image generation
Full audit log · Plugin system · S3 or local storage · Obsidian + Nexus context graphs
DEMO COMPANY
Real Estate Listing Agency
3 AGENTS
LIVE
The simplest Autonoma org that runs a full real-estate listings business end-to-end — no humans required.
Agent Org Chart
CEO Agent
Listing
Agent
Lead &
Booking Agent
CEO Agent — orchestrates, delegates, oversees budget
Listing Agent — creates listings, generates AutoHDR images
Lead Agent — follows up, qualifies leads, books appointments
End-to-End Workflow
1
Property Intake
CEO receives new listing data — address, specs, price
2
AutoHDR Generation
Listing Agent generates polished HDR listing images via AutoHDR
3
Listing Published
Listing Agent publishes to MLS, site, and social channels
4
Lead Capture
Inbound inquiries routed to Lead Agent automatically
5
Follow-Up & Qualify
Lead Agent sends follow-ups, answers questions, scores intent
6
Appointment Booked
Qualified leads get calendar invite — showing is scheduled
Instance Specs
Agents
3
Budget
~$90/mo
Deploy Time
< 1 day
AutoHDR
✓ Enabled
Human Gates
Optional
Uptime
24/7
Minimum viable agent org · 3 agents · full listing-to-appointment pipeline · AutoHDR image generation · ~$90/mo
WHO WE SERVE
The Middle Market Is Our Market
Not too small to spend. Not so big they have a procurement department for everything.
👤
Solo / Personal
Businesses
NOT A FIT
$0 – $200K ARR · 0–1 employees
No meaningful payroll to displace. Revenue opportunity too small to justify deployment cost. They're already lean.
No salary consolidation opportunity
Single operator — no delegation needed
Price-sensitive, low LTV
🎯
Middle Market
Businesses
OUR SWEET SPOT
$200K – $20M ARR · 2–50 employees
Spending real money on headcount. Aware of AI but lack infrastructure to deploy it. High motivation to consolidate costs and scale without hiring.
$80K–$120K/yr per role we displace
Autonoma captures a share of that savings
Enough complexity to need full org chart
Price-tolerant if ROI is clear
🏢
Large Enterprise
& Chains
NOT A FIT
$20M+ ARR · 50+ employees
Already have IT budgets, enterprise AI contracts, procurement cycles, and compliance teams. Overkill to sell to them at our stage.
Long sales cycles (12–18mo)
Procurement & compliance blockers
Enterprise AI solutions already in place
Revenue Model: A business paying $300K/yr in salaries → Autonoma replaces 3–5 roles → captures 10–20% of displaced salary → $30K–$60K ARR per customer
MARKET
$180B+ Addressable Market
33M
SMBs in the US
Every business that runs on 5–20 people is a potential customer
$180B+
Total Addressable Market
Global SMB operations & outsourcing spend
2026
The Inflection Point
AI agents are capable enough. Infrastructure has been the gap — until now.
Why Now
AI Agents Are Ready
Claude, Gemini, GPT-4 can complete real end-to-end business tasks without constant supervision.
The Cost Is Right
Running 25 agents for a month costs less than one day of human labor.
Infrastructure Gap
No production-grade orchestration layer exists for running agent companies — Autonoma fills it.
COMPETITION
We Are Not a Framework. We Are the Company OS.
Autonoma
LangGraph
CrewAI
n8n
Asana + AI
Runs entire companies
✓
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Org charts + reporting lines
✓
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Budget enforcement
✓
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Multi-company isolation
✓
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Governance + approval gates
✓
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Human-in-the-loop controls
✓
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Multi-provider model routing
✓
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Live deployments today
✓
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The only platform with live company deployments running today.
AGENTIC
ORCHESTRATION
DEEP DIVE
How Autonoma handles job locking, thread synchronization,
barriers, deadlock prevention, and concurrent agent execution
at production scale.
Job Locking
Mutex & Semaphores
Barriers
Lock-Free Structs
Deadlock Prevention
Context Sync
THE CHALLENGE
Why Agent Orchestration Is Hard
AI agents aren't simple functions — they're long-running, stateful processes competing for shared resources.
⚔️ Resource Contention
Multiple agents targeting the same API, database row, or file simultaneously — without coordination, data corruption occurs.
🔁 Non-Deterministic Execution
Agent runtimes vary wildly — an LLM call takes 2s or 45s. Sequential assumptions break. Concurrency requires explicit sync.
💀 Deadlock Risk
Agent A waits for Agent B's output while B waits for A's token budget approval. Classic circular dependency in agent form.
📉 Context Drift
Sub-agents lose task context through deep delegation chains. Hierarchical instruction passing degrades like a game of telephone.
🔀 Race Conditions
Two agents update the same company budget simultaneously. Without atomic operations, spend tracking goes negative.
🧱 Cascade Failures
One blocked agent stalls a whole org hierarchy. Without circuit breakers, one LLM timeout can freeze an entire company.
JOB LOCKING
Job Locking — Preventing Duplicate Execution
The Problem
A heartbeat fires every 60s. If Agent A's last heartbeat is still running when the next one triggers, you get two concurrent instances of the same agent — double-spending, duplicate outreach, conflicting decisions.
Autonoma's Solution
1
Claim Phase:
Agent requests exclusive lock on its job_id before execution starts. Stored atomically in PostgreSQL with a TTL.
2
Heartbeat Renewal:
Lock TTL refreshed every N seconds while agent is active. If agent crashes, lock auto-expires — no manual cleanup.
3
Contention Check:
Competing heartbeat trigger sees lock held → backs off gracefully. No error, no crash, no duplicate work.
4
Release Phase:
Lock released atomically on completion or error. Audit log records lock lifecycle for every job.
Lock Lifecycle
HEARTBEAT
TRIGGERS
CLAIM LOCK
(atomic)
EXECUTE
AGENT
RELEASE
LOCK
AUDIT
LOG
LOCK HELD
→ SKIP
↑ Contention Path
TTL-based auto-expiry prevents lock starvation on agent crash
DEADLOCK PREVENTION
Deadlock Prevention & Detection
Classic Agent Deadlock
CEO
Agent
Budget
Approval
CFO
Agent
Task
Delegation
DEADLOCK
CEO waits for Budget Approval
Budget waits for CFO sign-off
CFO waits for CEO task delegation
→ Circular wait, no progress
Autonoma's Prevention Stack
Resource Ordering
All agents acquire locks in a globally consistent order (by resource ID). Breaks circular wait by design.
Timeout + Backoff
Every lock acquisition has a deadline. On timeout, agent releases all held locks and retries with jitter — no starvation.
Wait-For Graph
Runtime tracks agent dependencies. Cycle detection runs on every new edge. Deadlock detected before it forms.
Hierarchical Priority
Higher org rank = higher lock priority. CEO agent preempts COO which preempts sub-agents in contention.
Async Approval Gates
Budget approvals never block the requester. Requests queue; agents continue other work while waiting.
CONTEXT SYNC
Hierarchical Context Preservation
The deeper the delegation chain, the more context degrades. Autonoma uses structured context propagation to prevent sub-agents from losing task intent.
❌ Without Context Sync
CEO Agent
Full company mission + Q3 goal + customer segment + budget
CMO Agent
Campaign goals + rough budget (mission context lost)
Content Agent
Write blog posts about X (why? unknown)
Writer Sub-Agent
Make content (customer segment? tone? lost)
Result: Sub-agent produces off-brand, off-goal content
✅ With Context Sync
Context Envelope: Immutable mission + goal snapshot attached to every delegated task
Compressed Summary: LLM-generated summary injected at each delegation hop
Dependency Graph (Nexus): Sub-agents query the graph for missing context on-demand
Obsidian Knowledge Base: Persistent company knowledge available to all agents at any depth
Context Budget Allocation: Optimal token distribution — deep agents get compressed parent context + full local task
Result: Every agent knows the mission, regardless of delegation depth