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.
Siloed models couldn't reason across domains; a supervisor now routes read, retrieval, and action agents across HR, IT, and finance.
25 agents were overwhelmed by 19k+ monthly compliance queries. Now, AI fields routine questions, freeing human experts for the toughest cases.
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.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
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.
Siloed models couldn't reason across domains; a supervisor now routes read, retrieval, and action agents across HR, IT, and finance.
25 agents were overwhelmed by 19k+ monthly compliance queries. Now, AI fields routine questions, freeing human experts for the toughest cases.
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.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.