AI workers with real output and real guardrails
Hire AI workers thatShip backlog
AgentFarms is built for teams who want more output, not more AI theater. Deploy workers with real tool access, hard role boundaries, and human approval where the stakes are real.
- Launch specialist workers across engineering, support, operations, and revenue
- Stop risky actions at the approval layer before they touch customers, code, or production
- Keep a searchable evidence trail for every task, decision, and shipped outcome
Deploy in under 10 minutes - no card required
PR #482 merged
Auth timeout fix for billing retries
Approval granted
Staging schema change cleared by reviewer
CI passed
985 of 985 checks green
Production rollout waiting for approval
Trusted by fast-moving teams
The problem
Most teams do not need more ideas. They need more execution.
Backlogs keep growing, hiring remains slow, and your best people still lose too much of the week to work that should already be off their plate.
10M+
open technical roles globally
Hiring cannot keep pace
Critical work waits because headcount is slow to secure, slow to onboard, and increasingly expensive to retain.
73 days
average time to hire
Delivery slows down
Cross-team handoffs, review queues, and repeated context switching quietly extend every release cycle.
$157k
average senior engineer salary
Capacity is costly
Expanding execution capacity with full-time hiring adds salary, benefits, management overhead, and onboarding drag.
40%
time lost to repeatable work
Experts get buried in routine work
Strong operators and engineers still spend large chunks of the week on boilerplate, follow-up, triage, and status work.
The solution
A governed execution layer for the work teams repeat every day
AgentFarms turns autonomous work into something operationally sane. Every worker has a role, a boundary, and a review model your team can actually live with.
- Role-based AI workers with clear responsibilities and expected outputs
- Direct connections into GitHub, Jira, Slack, email, and internal tools
- Approval checkpoints before risky changes, sends, or production moves
- Evidence for every action so reviews are fast and accountability stays clear
- Faster execution on repeatable work without adding full-time headcount
- Predictable rollout with policy, persona, and connector controls from day one
PR #482 opened
Auth timeout fix prepared for billing retries
CI checks passed
985 of 985 tests green
Approval needed
Customer schema migration ready for review
Platform
One platform for every step
of controlled AI execution.
Instant deployment
Launch a specialist worker in under 10 minutes. No custom setup scripts, no prompt engineering. Pick a role, connect your tools, deploy.
12 specialist roles
From Backend Developer to Customer Support Executive — every role has a defined scope, toolset, and approval model built in.
Approval gates
High-risk actions stop automatically for human review. Workers classify every action by risk level before touching production systems.
18 connectors
GitHub, Jira, Slack, Salesforce, Linear, and 13 more. OAuth-based, auto-refreshed, and isolated per workspace.
Full evidence trail
Every action, decision, and output is logged with timestamps and context. Searchable audit-ready evidence for ops and compliance.
Azure isolation
Each workspace runs in dedicated Azure infrastructure. Role boundaries, network isolation, and RBAC enforced at the platform level.
Customer stories
Real teams. Measurable outcomes.
Founders and operators use AgentFarms when they want more output without introducing a new black box into the business.
Test coverage 61% to 94% in 3 weeks
“We aimed the QA worker at our regression suite and stopped pulling product engineers into repetitive coverage work. The payoff showed up almost immediately.”
Sarah Chen
CTO - BuildFast
Feature cycle time reduced by 42%
“Our backend worker handles the repeatable implementation layer so human engineers can stay focused on architecture, judgment, and customer-facing decisions.”
Marcus Webb
VP Engineering - TechCorp
MVP shipped in 6 days
“As a small team, AgentFarms gave us execution capacity we simply did not have. We moved like a much larger company without losing review discipline.”
Priya Nair
Founder - ShipIt
Review cleanup time down 35%
“The Git and CI loop feels native. Work arrives structured, evidence is attached, and senior reviewers spend less time cleaning up process noise.”
James Okafor
Engineering Manager - DevOps Inc.
Velocity doubled with 3 workers
“The approval model is what made this viable for production. We gained output without treating governance like an afterthought.”
Anita Russo
Lead Architect - CloudNative
2 hours per week reclaimed from coordination
“The notifications, summaries, and evidence stream cut down so much back-and-forth. We spend more time deciding and less time chasing status.”
Tom Lindstrom
Head of Product - StartupX
Pricing
Pricing that matches how cautious teams actually buy
Start narrow, prove the workflow, and expand only when the output justifies broader rollout.
Starter
Best for teams proving governed AI workflows with one or two core roles.
- 2 AI workers
- Up to 10 repositories or toolspaces
- Core integrations and approval routing
- 2,000 task executions per month
- Evidence trail and baseline analytics
- Email support
Pro
For teams expanding governed execution across multiple workflows and functions.
- 5 AI workers
- Unlimited repositories and workspaces
- Expanded integrations across delivery and operations
- 10,000 task executions per month
- Priority support and custom approval workflows
- Team analytics and rollout visibility
Enterprise
For regulated environments that need scale, governance, and deployment flexibility.
- Unlimited AI workers
- Unlimited task executions
- SSO, SAML, and enterprise controls
- Tenant-isolated runtime options
- Dedicated onboarding and support
- Custom connectors and policy packs
FAQ
Frequently asked questions
Clear answers about setup, governance, pricing, and everyday operations.
Increase output without lowering your standards
Start with one painful workflow, connect the tools you already trust, and scale only after the review loop proves the worker belongs there.