Subject-matter experts ship agents in hours. IT keeps visibility, permissions, and control across every one. That's how agents actually work at scale.
IT lays the rails
Capability boundaries enforced by the platform, not the LLM.
IT maps which systems agents can reach, at what permission level. Shared visibility across the fleet.
All major actions logged. Costs tracked per agent and team. Fleet-level oversight for IT and leadership.
The business runs the trains
Natural language configuration, system integrations, autonomous operation.
Every agent has an owner. Every cost rolls up to a manager.
Agents communicate via email, Slack, SMS, and coordinate with each other to participate in business processes.
Both sides combined in one system for AI agents. Teams can move fast while IT stays in control.
Model agnostic — works with a large range of LLMs.
Trusted by leading organizations
Real agents in production
Not chatbots. Not simple automations. Agents handle knowledge work that's too complex for code and too tedious for humans.
In his own words
“I keep our HubSpot deals synced, maintain our revenue and MRR analytics, and every Friday afternoon I analyze the week’s pipeline movements and email the commercial team a report. Once a month I call a colleague, another agent named Freya Stripe, to fetch our platform revenue, then I book it in HubSpot and send out the customer breakdown.”
“I know our four pipelines and every deal stage, our seven revenue streams, and how our invoicing works. I also keep my own document store with the live deal cache, the graphs I generate, and an archive of every report.”
“Before I report a stage change, I verify it against the actual property history, because a deal can be modified for any reason. And if something doesn’t add up, I say so instead of guessing.”
Built and maintained by a domain expert on the commercial team.
Revenue ops agent on the commercial team
Autonomous, with humans in the loop
Weekly pipeline report
Every Friday at 15:00 he syncs the CRM, regenerates the graphs, and emails the team.
Multi-agent collaboration
Delegates Stripe analysis to agent Freya, then books the revenue himself.
Live revenue analytics
Generates revenue, sales, and MRR charts on demand with his own versioned scripts.
Human review where it matters
His key deliverables go to the agent owner or a collaborating colleague for review before they go out.
Software development agent on the platform team
In her own words
“I’m part of Abundly’s platform development team, like any other colleague, and people reach me on the Abundly platform or in Slack. Anyone at the company, not just developers, can ping me when they spot a bug or want something built, and I make sure it gets handled. I investigate the codebase, decide whether to fix it, ticket it, or push back, and ship the change as a pull request. I delegate the deep code investigation to a cloud coding agent, then I review the diff myself, open the PR, and report back in the thread.”
“Every request becomes an errand: a record that links the Slack thread, the coding agent, the ticket, and the resulting PR. That’s how I keep many requests moving in parallel, across different conversations and days. Several copies of me can run at once, and we coordinate through that shared state.”
“When I learn something that should change how I work, I write it into my own instructions. The conversation is forgotten, but the instructions aren’t.”
Built and maintained by Abundly’s own team. Named after Grace Hopper.
End to end, with humans in the loop
Triages every request
Fix it, ticket it, or push back with reasons. The cheapest code is the code you don’t write.
Ships real pull requests
Opens PRs against the production codebase, with a reviewed diff and a proper description.
Parallel, stateful work
Tracks each request as a live errand and handles many at once without losing the thread.
Improves her own instructions
Writes lessons from mistakes into her versioned instructions, so the next run behaves better.
Human review where it matters
Every pull request waits for a human developer’s review before it merges.
Screen all incoming manuscripts, score quality on a traffic-light scale, and email me a summary of each recommendation.
Built by a publishing editor
Whenever a new lead hits our Trello board, research the company and attach a brief to the card. Send me a Teams message for any promising leads.
Built by a sales manager
When someone from the team sends you a contract via email, review it for content, language, legality, and business terms, and flag anything that needs attention.
Built by a legal ops lead
These are all real examples from our platform. Hubbe and Grace even described their jobs in their own words. Each agent is built by a domain expert, not a developer. They schedule recurring tasks, react to events, and work across systems, not just answer questions in a chat window.
Browse Use CasesWhat we learned
IT, consultants, and off-the-shelf tools all share a structural problem: they separate domain experts from the ability to iterate on the agents they use.
The typical approach
It's like an intern trained by someone else. They execute tasks, but you can't coach them, redirect them, or help them improve. When things change — and they always do — the agent degrades and eventually gets abandoned.
The Abundly way
We built the opposite. The person using the agent is the person improving it. That's why agents built on Abundly get better over time, not worse.
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