Agents that think, act and deliver.
Build autonomous agents on the best open models, give them skills and memory, and govern every action — from prototype to production fleet.
What holds ai agents teams back
Demos that never ship
Agent prototypes impress in a notebook and then crumble on real data, real tools and real users.
Black-box behaviour
When an agent fails, nobody can see why — so nobody trusts it with anything important.
Model lock-in
Hard-coding one model means every new release becomes a migration project.
Six ways to put AI to work, today
Autonomous agents that reason, act and deliver.
Research analysts
Agents that search, read and brief you with cited sources in minutes.
Operations runners
Recurring back-office tasks executed on a schedule with approvals.
Support resolvers
Agents that diagnose issues, take safe actions and escalate cleanly.
Data wranglers
Clean, reconcile and transform datasets, then explain what changed.
Coding assistants
Agents that draft, test and document changes for human review.
Multi-agent teams
Specialists that hand off work to one another toward a shared goal.
From first prompt to everyday workflow
- 01
Define the goal
Describe the outcome and success criteria in plain language.
- 02
Grant skills
Choose exactly which tools and data the agent can use.
- 03
Test in the playground
Watch runs step by step and refine with real inputs.
- 04
Deploy & monitor
Schedule, trigger via API and track every run.
Agents built for ai agents
Give an agent a goal and it plans the steps, uses the skills you grant and reports back — with approvals wherever you want a human in the loop.
- Research analysts: Agents that search, read and brief you with cited sources in minutes.
- Operations runners: Recurring back-office tasks executed on a schedule with approvals.
- Support resolvers: Agents that diagnose issues, take safe actions and escalate cleanly.
- Done
Understand
Intent & context
- Done
Plan
Break into steps
- Running
Act
Run skills & tools
- Queued
Deliver
Verified result
What changes when you switch it on
- Outcome 1
From demo to production
Guardrails, logs and fallback make agents reliable enough to deploy.
- Outcome 2
Every run explained
Step-by-step traces turn mysterious failures into quick fixes.
- Outcome 3
Future-proof by default
Routing lets agents adopt better models without code changes.
Foundational models, plus proprietary architecture
Every leading open model family, made sharper by the layer we built around them.
Routing
The best model for each request, automatically.
Memory
Your ai agents context, remembered securely.
Skills
Search, read, write and call your own tools.
Agents
Goal-driven workers with guardrails.
All controls healthy
Continuously monitored
- Encryption in transit & at rest
- SSO & scoped API keys
- Audit logs for every action
- Your data never trains shared models
Enterprise-grade from day one
- Your data is never used to train shared models
- Encryption in transit and at rest
- Roles, scoped API keys and approval steps
- Audit logs, SSO and private deployment options on higher plans
AI Agents questions, answered
Can agents use my internal tools?
Yes. Wrap any API as a skill and grant it to the agents that need it, with scoped permissions.
How do I stop an agent doing something risky?
Require approval for sensitive skills. The agent pauses and waits for a human decision.
Can agents work together?
Yes. Agents can hand off tasks to one another and share workspace memory.
Which models do agents use?
Auto-routing chooses per step, or you can pin specific models for predictability.
Bring AI to ai agents — today
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