AI Agents that run work for you
Move beyond chat. Build agents that watch for signals, take action across your tools, and keep work progressing without manual follow-ups. From prep to execution, agents handle the busywork so teams stay focused.
Trusted by enterprises
What Are AI Agents In WorkLLM?
Most work doesn’t break at the thinking stage. It breaks at execution when someone forgets to follow up, update a doc, send a message, or act on a signal. AI Agents exist for that gap. AI Agents are trigger-based, automated workflows that turn events into actions across the tools your team already uses.
Each agent:
- Listens for signals (emails, updates, conditions)
- Applies logic and context
- Takes action automatically (notify, summarize, update, trigger a tool)
- Keeps work moving without manual follow-ups.
Think of AI Agents as execution engines, not chat experiences.
Why Team Needs AI Agents
Work breaks down not because teams lack insight, but because execution is manual and fragmented. AI agents turn signals into actions automatically, keeping work moving across tools without constant follow-ups. They reduce handoffs, eliminate busywork, and ensure nothing falls through the cracks.
Build AI Agents In Minutes
Create personal AI agents that act across your tools using simple, predictable rules — no code required.
1) Provide basic info
Give your agent a clear name and description so you know exactly what it does and when it should run. This helps you manage and recognize agents as you create more over time.
2) Select apps with access
Choose which apps and data sources the agent can use. Agents only operate within the access you explicitly allow.
3) Define rules
Set when the agent runs and what it should do using triggers, conditions, and actions. Rules keep agent behavior precise and predictable.
4) Choose notifications
Decide how and when you want to be notified — immediately, daily, or weekly — so agent outputs arrive on your terms.
The WorkLLM Advantage
Most platforms treat agents as basic automations or fragile workflows. WorkLLM delivers enterprise-grade AI Agents built to execute real work — with control, context, and governance.
Context Aware Execution
WorkLLM Agents don’t blindly fire actions.They understand conversations, documents, history, and intent before acting — so drafting a reply, generating a proposal, or preparing a meeting actually reflects what’s happening, not just that something happened.
Fully Configurable Rules
Define exactly when an agent runs, what conditions apply, and what actions it should take. From simple email follow-ups to cross-app execution across teams, agents behave predictably and transparently — no hidden logic, no surprises.
Enterprise Governance Built In
Permissions, visibility, auditability, and ownership are part of the agent model from day one. Admins control who can create, edit, run, or share agents — ensuring AI-driven execution stays aligned with company policies and compliance requirements.
Designed to Evolve With How You Work
AI Agents in WorkLLM are not fixed workflows. As your tools, responsibilities, or priorities change, agents can be adjusted without rebuilding logic from scratch — keeping execution aligned with how work actually evolves inside organizations.
Human-in-the-loop By Default.
WorkLLM Agents are built to assist execution, not silently act behind the scenes. Drafts, recommendations, and actions are visible and reviewable — ensuring humans stay in control while AI handles the heavy lifting.
Reduces cognitive load, not just tasks
Agents don’t just automate steps — they reduce the mental overhead of remembering follow-ups, checking states, and coordinating across tools. This frees teams to focus on higher-value decisions instead of operational vigilance.
FAQs
What is an AI Agent in WorkLLM?
An AI Agent is a personal, rule-based AI that monitors your tools, understands context, and takes action automatically based on how you configure it.
How are AI Agents different from AI Assistants?
AI Assistants answer questions on demand. AI Agents go further. They execute work continuously based on triggers, conditions, and actions you define.
Are AI Agents shared across teams?
No. AI Agents are personal by design. Each agent runs only for the individual who created it, ensuring clarity, accountability, and predictable behavior.
What kinds of tasks can AI Agents perform?
Agents can draft emails, send reminders, prepare meetings, qualify leads, generate proposals, coordinate launches, monitor workflows, and more across connected tools.
Which tools can AI Agents connect to?
AI Agents can connect to email, calendars, documents, engineering tools, CRM, support tools, and other business systems. More integrations can be added over time.
Do AI Agents act automatically or require approval?
By default, agents generate drafts and recommendations that you review. Actions are transparent and configurable, keeping humans in control.
Can AI Agents send emails or messages on my behalf?
Agents can draft messages and prepare responses. Automatic sending can be restricted or enabled based on organizational policies and user preferences.
How are AI Agents triggered?
Agents can be triggered based on events (like a new email or upcoming meeting), schedules (daily or weekly), or on specific time.
What conditions can I apply to an AI Agent?
Conditions allow agents to run only when specific criteria are met, such as sender, keywords, date ranges, response status, or project identifiers.
Can I edit or pause an AI Agent after creating it?
Yes. You can update rules, actions, or notifications at any time, or pause an agent temporarily without deleting it.
Are AI Agents secure?
Yes. Each agent operates only within explicitly approved access boundaries. Permissions, access controls, and auditability are built into the platform.
Can administrators control AI Agent behavior?
Admins can define which apps can be connected and maintain visibility for governance and compliance.
How is this different from automation tools like Zapier or Make?
Traditional automation tools rely on rigid triggers and steps. WorkLLM AI Agents understand context and intent before acting, making them more flexible and reliable for real work.
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