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.
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.
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.
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.
As soon as our database over investment prospects is updated, analyze each new entry against our criteria — market size, team experience, competitive landscape, and funding history.
Built by an investment analyst
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 — flag anything that needs attention.
Built by a legal ops lead
These are all real examples from our platform. Each built by a domain expert, not a developer. Agents on our platform 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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In 4-6 weeks, build one high-impact agent while training your core team. See real results with minimal commitment.
Work With UsPlatform access, ongoing training, agent co-development, and strategic coaching — everything you need to build and run AI agent operations across your organization.
Work With UsOpen enrollment programs for individuals and teams ready to master agent design and implementation.
Browse CoursesNot sure which path is right for you? Talk to us.
Let's discuss how we can help your organization succeed with AI transformation.
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