Agent management
Give each employee, team, or role a managed AI agent with its own runtime identity and admin-controlled access.
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Private AI agent platform
Run CLI coding agents under company control
Bewize gives companies a private management layer for Hermes, Claude Code CLI, and OpenAI Codex CLI agents. Employees use the agents for real work; admins control deployment, tenant isolation, approved skills, storage, logs, usage, and model spend.
What Bewize sells
A private platform for operating employee AI agents as company infrastructure instead of unmanaged personal chatbot accounts.
Secure runtime boundaries
Keep memory, sessions, browser profiles, workspaces, logs, and secrets separated per employee, team, or agent.
Learn moreOperational visibility
Attribute runs, schedules, model usage, token usage, and costs before AI spend turns into a black box.
Learn more01
Managed work agents for every employee
Bewize gives each employee, team, or operational role a company-owned agent identity. Admins control the agent's runtime, memory, tools, storage, credentials, logs, and behavior instead of leaving work inside unmanaged personal AI accounts.
Assign agents to people, teams, and roles
Create agents for employees, departments, service desks, engineering teams, or recurring operational roles.
Keep work inside the company boundary
Agent memory, files, logs, and outputs stay under the company's selected deployment boundary.
Use approved agent runtimes
Run Hermes, Codex-style, Claude-style, or other approved agent runtimes under one operating model.
Connect only approved resources
Connect agents to approved skills, storage, credentials, and policies.
Operational outcome
Employees get practical AI help for real work while the company keeps ownership and control of the agent identity.
02
Operate every agent from one control plane
Bewize gives platform teams a single place to create agents, block access, wake idle runtimes, stop active work, inspect state, manage schedules, and roll out runtime releases. Agents become managed infrastructure instead of unowned scripts running across teams.
Central registry
Track which person, team, or role owns each agent and where operational controls apply.
Lifecycle controls
Wake, stop, disable, inspect, and schedule agents without manual server debugging.
Runtime policy
Decide which tools, models, credentials, adapters, and runtime features each tenant may use.
Release operations
Publish, activate, pin, or roll back approved agent runtime versions.
Audit visibility
Connect runs, schedules, state changes, and policy actions to the agent registry.
Operational outcome
Your platform team gets one operating model for managed agents instead of scattered runtimes and unclear ownership.
03
Private deployment boundary for agent operations
Bewize is built for companies that need AI agent infrastructure inside a controlled host or on-prem environment. Operations teams keep ownership of network access, credentials, storage, logs, updates, adapters, and runtime policy.
Deploy where sensitive workloads already live
Deploy agent infrastructure where the enterprise already governs sensitive workloads.
Keep runtime assets inside the boundary
Keep workspaces, logs, secrets, storage, adapters, and runtime state inside the selected deployment boundary.
Operations-owned controls
Let internal operations teams control upgrades, connectivity, credentials, and access policy.
No unmanaged chatbot ownership
Run managed employee agents without moving operational ownership to unmanaged personal chatbot accounts.
Operational outcome
Teams can adopt AI agents while keeping deployment ownership with the enterprise operations team.
04
Tenant-isolated runtime for every agent
Each employee, team, or role agent runs inside its own tenant boundary. Bewize keeps memory, sessions, browser profiles, logs, secrets, files, and runtime state separated so one agent does not inherit another tenant's private context.
Distinct runtime identity
Each employee, team, or role agent gets a distinct execution identity.
No reused browser state
Browser cookies, sessions, and profiles are not reused across tenant boundaries.
Attributed files, secrets, and logs
Logs, files, managed secrets, and runtime state are attributed to the owning tenant.
Policy-defined tenant boundary
Admin policy defines which tenant boundary each agent belongs to.
Operational outcome
Companies can deploy many managed agents without merging private user, team, browser, or workspace state.
05
Controlled runtime release management
Agent runtimes change quickly. Bewize gives admins a release process for approved runtime versions, tenant pinning, staged activation, and rollback when an update breaks a workflow.
Approved runtime registry
Publish approved runtime versions from one controlled release surface.
Tenant pinning
Keep specific teams or workflows on a known-good runtime version.
Staged activation
Move selected tenants forward before activating a release broadly.
Rollback target
Return affected tenants to a previous working runtime without rebuilding each tenant manually.
Operational outcome
Agent upgrades become an operations-managed release process instead of per-employee runtime drift.
06
Approved skill packs for company procedures
Bewize gives companies a governed place to package reusable agent skills, prompts, runbooks, and procedures. Admins install approved packs into the employee, team, or role agents that need them.
Approved skill repository
Keep reusable capabilities, prompts, runbooks, and procedures in one governed source.
Installable skill packs
Ship business, engineering, support, operations, or company-specific packs to selected agents.
Controlled installation targets
Decide which agents receive each approved capability set.
Repeatable agent behavior
Give similar agents the same reviewed procedures instead of copying prompts by hand.
Operational outcome
Agent behavior becomes consistent enough to review, improve, and operate as company procedure.
07
Durable agent workspaces and shared storage
Bewize gives each managed agent a durable workspace and connects approved agents to shared company storage. Reports, drafts, ledgers, snapshots, context, and team files stay in controlled locations instead of disappearing into chat history.
Per-agent durable workspace
Agents can write reports, drafts, ledgers, snapshots, and other outputs that survive a session.
Shared company context
Teams and trusted agents can work from selected company documents and operational data.
Controlled read and write policy
Access follows employee, team, tenant, and workflow boundaries.
Storage-ready outputs
Agent work can become files, records, or reusable context for future company work.
Operational outcome
Agent output becomes part of company work storage instead of disposable chat fragments.
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Usage ledger and model spend controls
Bewize records agent runs, requests, model usage, token usage, schedules, lifecycle events, and outcomes in a usage ledger. Admins can attribute activity by employee, team, agent, tenant, day, and model before spend becomes an unexplained provider bill.
Run and request visibility
Inspect runs, request history, lifecycle events, schedules, and outcomes.
Usage attribution
Roll up model and token usage by employee, team, agent, tenant, day, and model.
Budgets, limits, and alerts
Apply budgets, limits, alerts, and policies for expensive models and tools.
Redacted business metrics
Track operational outcomes without exposing task content when it should remain private.
Operational outcome
Admins can connect AI spend to the agents and teams that caused it, then control future usage with platform-level budgets and limits.
09
Chat adapters for managed agents
Employees can work with agents from familiar chat-style channels while Bewize keeps adapter routing, tenant policy, replies, media handling, and logs under platform control. Current source-backed channels are HubChat, WhatsApp, and Telegram.
Source-backed channels
HubChat, WhatsApp, and Telegram are the implemented/source-backed adapter set.
Platform-routed messages
Messages route through Bewize before reaching managed agents.
Media and replies
Media handling, outbound replies, and voice transcription are source-backed capabilities.
Roadmap channels stay separate
Slack, Microsoft Teams, Discord, and corporate messenger stay roadmap/directional until implementation evidence exists.
Policy and logs
Admin policy, logs, and tenant controls remain part of the agent operating layer.
Operational outcome
Employees can use agents from supported channels without turning agent communication into unmanaged personal chat infrastructure.
10
Browser assistance for employee workflows
Bewize's browser-extension direction is to connect a company-managed personal agent to the employee's real browser work. The target UX keeps the employee aware of page context and assisted actions, with automation promoted only after review.
Page-aware assistance
Target help for the form, dashboard, or workflow already open in the employee browser.
Visible browser actions
Keep browser manipulation visible so the employee can see and control agent actions.
Automation candidates
Repeated browser steps can become automation candidates instead of hidden background automation.
Reviewed promotion path
Promote repetitive work only after review into a governed agent workflow.
Operational outcome
Employees can get help inside real browser work while the company controls context, action visibility, review, and automation boundaries.
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Find repeated work before automating it
Bewize's process-mining direction is to use governed browser and agent workflow signals to identify repeated work and propose automation candidates for review. This stays framed as a target UX until implementation evidence exists.
Observed workflow signals
Browser steps and agent run patterns become inputs for analysis.
Repeated pattern detection
Recurring tasks, handoffs, forms, and operational loops can be grouped into candidates.
Review before automation
Suggestions pass through admin or operator review before becoming workflows.
Governed promotion
Approved automations should inherit logs, policy, and tenant controls.
Operational outcome
Automation priorities come from observed repetition, not generic guesses about what employees might automate.
FAQ
What am I buying?
A private AI agent platform for your company: managed employee agents, admin controls, tenant isolation, runtime releases, skill packs, shared storage, usage tracking, cost controls, and communication adapters.
Can Bewize run in a private environment?
Yes. Private-host and on-premise deployment are core positioning points. Public claims should still stay aligned with source-backed implementation evidence.
Which communication adapters are implemented?
The current source-backed list includes HubChat plus WhatsApp and Telegram adapters. Slack, Microsoft Teams, Discord, corporate messenger, Chrome extension, and process mining should be described as directional until implementation evidence exists.
Deploy private AI agents for your company
Request a demo to map your employee-agent rollout, hosting boundary, admin controls, skills, storage, communication channels, and usage controls.
+1 332 2081410
enterprise@bewize.ai