Teams shipping real work with autonomous agents
From fast-growing startups to global operations teams, builders use our platform to put autonomous AI agents into production — resolving tickets, closing books, and shipping code while humans focus on the hard calls.
- 50+ teams in production
- SOC 2 Type II
- Updated 2026
Results across our customer base
Numbers reported by teams running autonomous agents on real workloads — measured against their pre-agent baselines over the last twelve months.
Hours saved
across all customers
Tickets automated
resolved end to end
Median ROI
first-year return
Faster cycle time
task start to done
Every figure below comes from agents running the same loop you can build today — perceive, reason, act, observe — wired to real tools, memory, and guardrails. These are not demos; they are workloads that run every hour of every day.
Companies building on AI Agentics
What teams built — and what it moved
Nine mini case studies spanning support, finance, engineering, and operations. Each headline metric is measured against the team's own baseline.
Quantio · Support triage
Built a tier-1 support agent that reads tickets, looks up account state, and issues refunds via tool calls. Result: 71% of tickets resolved without a human, first-response time down from 6h to under 90s.
Helios · Finance ops
Deployed a month-end close agent that reconciles ledgers across four systems. Result: book-close time cut from 9 days to 2, with 100% of journal entries traceable to source.
Nimbus · Eng productivity
A coding agent reproduces bugs, writes fixes, and opens PRs with passing tests. Result: 3,200 engineer-hours saved per quarter and a 40% drop in stale issues.
Vertex · Market research
Research agents gather sources, cross-check facts, and synthesize cited briefs. Result: analyst report turnaround dropped from 3 days to 4 hours at 2.6x the volume.
Lumen Labs · Sales enablement
A lead-enrichment agent scores intent and drafts grounded outreach. Result: 5x more accounts touched per rep and a 28% lift in qualified-meeting bookings.
Cobalt · SRE remediation
An on-call agent triages alerts and runs safe remediation playbooks. Result: 54% of incidents auto-resolved and mean time to recovery cut by 63%.
Northwind · Back office
An invoice agent extracts line items, matches POs, and posts approvals. Result: 480 hours/month of manual data entry eliminated with a 99.4% match accuracy.
Aether · HR onboarding
An onboarding agent provisions access, schedules sessions, and answers policy questions from a RAG knowledge base. Result: new-hire setup time down 80%.
Monarch · Localization
A multi-agent pipeline translates, reviews, and QAs content across 14 markets. Result: launch lead time cut from 5 weeks to 6 days with consistent brand voice.
Different teams, one pattern: an agentic AI system wired to the right tools and memory, supervised by humans where the stakes are highest. Browse the full library of agentic AI use cases to see which maps to your workflow.
What builders tell us
The teams shipping agents care less about model benchmarks and more about reliability, observability, and getting work off their plate.
We replaced a backlog, not a person. Our support agent clears the repetitive 70% so the team can actually solve the hard tickets that need a human.
The tracing was the unlock. Being able to replay every decision and tool call is what let us trust an agent with the month-end close.
Our agent opens PRs while we sleep. By morning there are tested fixes waiting for review — it changed what a small team can ship.
Automation rate by team
Share of in-scope work that customer agents now complete end to end, without a human in the loop, across common functions.
Work resolved autonomously by team
The common thread across every rollout
Start narrow, then expand
The teams above did not boil the ocean. Each shipped one well-scoped agent against a high-volume, repetitive workflow, wired it to a handful of trusted tools, and instrumented every decision so they could debug and replay. Once the first agent earned trust, they widened its scope and added neighbours. Reliability and security — not raw model size — were the deciding factors in every production rollout.
Put your first agent into production
Join the teams shipping real work with autonomous agents. Start free, instrument everything, and scale what works — no credit card required.