AI agents on the workstation
See, control and coach the AI coding agents your employees run, and the MCP servers they call.
Codex
Claude Code
GitHub Copilot CLI
Cursor
Devin Desktop
OpenCode
Control AI agents where they run.
See how every person works with AI agents on their workstation, across every tool, coach them in the moment, and enforce policy right where the work happens.
Supported integrations
See
See what every agent did.
What every agent did, across every tool, tied to person, repo, PR and ticket. The commands, MCP servers, failures and retries behind every prompt, across every supported tool.
See
Follow the spend to the people and the work.
Break cost, active users, sessions and tokens down by AI agent, team and model, over any time range, and see what the fleet spends and where it goes.
Assets
Know what your teams are using.
See the workstations, AI agents, models, MCP servers and skills across your fleet. Explore adoption, and find the ones that need attention.
Budgets
Put your AI budgets into practice.
Track spend against budgets and forecasts. Set budgets by organization, team, user or model, and choose how TokenShift responds when a limit is reached: warn, block, or require approval.
Control
Enforce policy where agents run.
Allow or block models, MCP servers, tools and skills on the workstation, and control what leaves the machine. Set different rules for public and private repos, and see each policy's reach and activity.
Coach
Help every person work better with agents.
Coach people in the moment: alerts before a budget cap is hit, and guidance on prompts, context and cache habits. Track how proficiency grows across teams, and see what is holding people back.
Routing
Match the model to the work.
Smart Routing is a separate, opt-in module. When enabled, it can select a different model based on the task and your organization's policies.
Investigate
Ask a question. Follow the evidence.
Investigate how AI agents are used with Analyst. Ask about teams, repos, models and types of work, then explore the charts behind the answers and ask follow-up questions.
Where the work happens.
TokenShift runs natively on employee workstations, at each AI agent, deployed through the MDM you already use.
Deployed through your MDM
Roll out to one team or the whole fleet with the device management you already use.
- Jamf
- Intune
- JumpCloud
Alongside your gateway
Keep your gateway. It governs the traffic; TokenShift governs the work on the workstation.
Closer to the work. More control.
A gateway sees the request. TokenShift sees the session, the code and the tools behind it, and acts on the machine.
Private by design.
Data is processed on the device. Prompts never leave the machine for a central store, and each person can see their own insights. Up to 30 days of history from day one of install.
Coaches the person in the moment.
TokenShift alerts people before they hit a budget cap and suggests better ways to work with their agents. Only something running beside the agent can say: you restart context every session even though your cache is cold, here is what to do instead.
Sees the work, not the request.
TokenShift records which tool and MCP server ran, each failure and retry, and how long it took, tied to the person, repo, pull request, ticket and session. A gateway sees at most what is passed into the next prompt.
Enforced where the agent runs.
Block an unapproved MCP server, catch sensitive data before it leaves the machine, and set different rules for public and private repos. In-session guidance shows why a policy applied.
Scales with the fleet.
Every workstation does its own processing, so there is no central proxy to size. TokenShift deploys through the MDM you already use and connects natively to each supported AI agent.
Each sees one tool. None sees the person.
Vendor consoles each report only their own tool, per user. TokenShift ties every supported agent to the person, team, repo and pull request.