Handoffs lose context.
Ownership and outstanding obligations become unclear as work moves between agents and people.
KLARA FOUNDRY / 01
Explore Klara
Reliability for agentic work begins with accountable operations.
01 / EXPLORE KLARACapable agents still need
Capable agents still need
accountable operations.
AI agents can do useful work. The difficult part begins when that work crosses people, tools, approvals and interruptions. Humans become the message bus—chasing status, restoring context and deciding what happens next.
Evidence goes stale.
Teams spend time determining which result, approval or status is still current.
Interruptions create uncertainty.
Restarts, tool failures and partial outcomes leave people reconciling what actually happened.
THE QUESTIONS THAT MATTER
What work is live? · Who owns it? · What authority exists? · Which evidence is current? · What needs management attention?
FIRST FEATURED TECHNOLOGYCommand
Command
Bridge.
Command Bridge is Klara Foundry's working internal prototype. It coordinates specialist AI work while maintaining operational context around ownership, obligations, evidence, authority and management attention.
Tell us about your workflowA CLEAR DISTINCTION
01
Working internally today
Command Bridge is used as an internal prototype. This is not a generally available enterprise deployment.
02
What we're exploring next
A broader reliability layer for agentic work across systems and workflows. That product direction still requires external validation.
NO INVENTED DASHBOARDS · NO PRODUCTION CLAIMS
A RECOGNIZABLE INTERRUPTION
When an AI worker stops mid-task
An illustrative workflow pattern based on Command Bridge's internal operational-state capabilities—not a performance claim or product screenshot.
An AI worker is interrupted. Who owns the next step, what remains open, and which evidence matters can become difficult to piece together.
The internal prototype maintains work ownership, outstanding obligations, and evidence context to support a more orderly handoff and recovery.
The manager can look at unresolved work and decisions instead of reconstructing every handoff manually.
Internal prototype capability illustration. No quantified time savings, guaranteed recovery, or production deployment implied.
WHO WE'RE LEARNING FROMFor teams past the
↗ ⌘ ◎
For teams past the
agent demo.
Technical founders
Moving agent experiments toward dependable work inside real products.
AI product & platform teams
Coordinating agents, tools and people across increasingly complex workflows.
Operational workflow owners
Managing approvals, exceptions, interruptions and accountability.
These are audiences we want to learn from, not validated customer segments.