Notion
Autonomous workflow agents
Rigid prompts limited AI to isolated tasks. Now, a central reasoning model coordinates agents to plan and execute complex workflows.
- Internal AI adoption across all teams
Teams spent hours manually prepping for meetings. Now, custom agents generate instant briefings, speeding up client decisions by days.
An HR technology company serving more than 5,000 global customers, providing a platform for managing employees and organizational culture.
Client decision-making processes often took days to finalize, while internal teams spent hours manually preparing for meetings and identifying upsell...
“We’re focused on allowing people to do more with more. Each agent has a role, just like each employee does. That’s what makes the system sustainable.”
Cloud-based human resources platform for global workforce management and payroll.
AI research and deployment company developing generative models and tools.
HiBob's Internal productivity tools is part of this use case:
Related implementations across industries and use cases
Rigid prompts limited AI to isolated tasks. Now, a central reasoning model coordinates agents to plan and execute complex workflows.
Underperforming agents meant engineers rewriting prompts by hand and guessing. Now a self-correcting system reads scores and rewrites them.
Siloed models couldn't reason across domains; a supervisor now routes read, retrieval, and action agents across HR, IT, and finance.
Reps were buried in manual research. Now, reasoning models draft strategic proposals and answer technical questions live.
HR queries wound through a patchwork of disconnected systems and a separate help desk. One Copilot agent replaced the whole stack.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Reaching a new market meant standing up another dubbing team. One API now dubs into 30+ languages; creators edit tracks before it goes live.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Teams spent hours manually prepping for meetings. Now, custom agents generate instant briefings, speeding up client decisions by days.
An HR technology company serving more than 5,000 global customers, providing a platform for managing employees and organizational culture.
Client decision-making processes often took days to finalize, while internal teams spent hours manually preparing for meetings and identifying upsell...
“We’re focused on allowing people to do more with more. Each agent has a role, just like each employee does. That’s what makes the system sustainable.”
Cloud-based human resources platform for global workforce management and payroll.
AI research and deployment company developing generative models and tools.
HiBob's Internal productivity tools is part of this use case:
Related implementations across industries and use cases
Rigid prompts limited AI to isolated tasks. Now, a central reasoning model coordinates agents to plan and execute complex workflows.
Underperforming agents meant engineers rewriting prompts by hand and guessing. Now a self-correcting system reads scores and rewrites them.
Siloed models couldn't reason across domains; a supervisor now routes read, retrieval, and action agents across HR, IT, and finance.
Reps were buried in manual research. Now, reasoning models draft strategic proposals and answer technical questions live.
HR queries wound through a patchwork of disconnected systems and a separate help desk. One Copilot agent replaced the whole stack.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Reaching a new market meant standing up another dubbing team. One API now dubs into 30+ languages; creators edit tracks before it goes live.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.