AI agents for the work
your team repeats
Build agents with your company context and tools. Run them on demand or on a schedule. Review the conversations, actions, and outputs.
Workspace
3 agents active · 11 integrations
Recurring work should not restart from scratch every time
Teams repeat the same reviews, handoffs, checks, and research each week. Give the agent the context, tools, environment, and review trail it needs to do the work again.
The same work comes back
Reviews, checks, research, and handoffs return on a weekly or monthly rhythm. Starting over each time wastes the context you already have.
Context lives in too many places
Policies, tickets, files, dashboards, and chat threads sit in different systems. Agents need that context in one place to run the job again.
Handoffs lose detail
When work moves between people or tools, steps and decisions drop out. Recurring jobs need a clear trail of what happened and what to do next.
Review still matters
Teams need to inspect the conversation, actions, and output for each run. Recurring work stays useful when review is built in.
How Prajvis runs recurring work
Give agents company context, files, tools, browser sessions, and computers. Run on demand or on a schedule. Keep run history so your team can review what happened and repeat the job.
Agents
Multi-agent teams that reason, delegate, and ship
Configure agents through detailed settings or build through conversation. Each agent gets its own identity, skills, tools, and LLM provider. Spawn sub-agents that work in parallel, delegating complex tasks across a hierarchy that runs autonomously.
- Multi-agent hierarchies with automatic delegation
- Git-based skill catalog: install, update, version
- Any LLM provider: swap models without rewriting agents
- Per-agent isolation with configurable security levels
Integrations
Every tool your agents need, built-in or bring your own
Browse the catalog or bring your own. Install tool servers via the Model Context Protocol (MCP), REST APIs, or custom hooks. Agents gain tools at runtime with no hardcoded connections and no vendor lock-in.
Computers
Sandboxed computers agents can see and control
Every agent gets a disposable computer with a browser session, files, and tools. Isolated per user, ephemeral by default, and observable from first click to final output.
Tasks
Run the work that comes back on a schedule
Schedule a prompt, or build a multi-step pipeline that collects data, runs tools, and gives prepared context to an agent. Prajvis handles scheduling, dispatch, and run history.
Collect metrics, analyze, and post summary to Slack
Query Datadog
Fetch PagerDuty
Analyze Metrics
Post to Slack
Review new documents and summarize key terms
Scan vendor profiles for compliance changes
Memory & Multitenancy
Shared agents, private memory for every user
Each authenticated user builds their own memory context. Recalled context is never shared across users, even when they use the same agent.
Flexibility
Every layer. Configurable.
Providers, integrations, and deployment are independent choices. Change any layer without touching the others. No lock-in at any level of the stack.
Swap LLM providers without rewriting agents. Route to the model that fits the task.
Connect tools through MCP, REST, or custom hooks. No vendor lock-in.
Our cloud, your VPC, or self-hosted. Prajvis can run fully air-gapped when your model provider or approved model endpoint is available inside your network.
Use cases
Start with work your team already repeats
Pick a recurring job, connect the tools it needs, and run it with reviewable conversations, actions, and outputs.
Weekly customer or account research
Collect updates from files, tools, and browser sessions. Prepare context for the agent, run on a schedule, and review the report before it goes to the team.
See it in actionSupport escalation investigation
Gather ticket history, related files, and tool output so the team can review what changed and what to do next.
Competitor or product change checks
Visit pages on a schedule, capture what changed, and leave a trail of actions and findings to review.
New-hire onboarding steps
Run the same onboarding checklist with company context, tools, and a reviewable record of each step.
Compliance evidence collection
Collect evidence from systems and files on a schedule, then export logs and outputs for review.
RevOps exception handling
Flag pipeline exceptions, pull supporting context, and prepare a clear package for human review.
SRE morning checks
Run morning health checks across tools and dashboards, then leave conversation, actions, and output to inspect.
Start with one piece of work your team repeats
Pick a recurring job. Connect the tools it needs. Run it in Prajvis. Your team gets the conversation, actions, and output to review.