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AI Agents to
Scale Your Team
Meet the leading platform for Agentic Process Automation. Used by Fortune 500 companies and scale-ups, the platform helps you automate manual workflows with AI agents to boost productivity and let you and your team focus on the work that matters most.
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Prepare your AI Agents to take on your task
The Agent Overview is your initial introduction to the agent. Here you will find a brief description of what the agent does, the tools assigned to it and the task templates it can execute when called into action.
Want the agent to refer to specific information that will help it execute tasks? Add that information, whatever file-type that may be in, to the Agent Database. The agent can now recall this information when needed.
Equip the agent with additional tools to help it execute tasks: choose from a list of predefined tools and get started in minutes. The tools section helps you browse through options and toggle them on/off depending on what you want the agent to be capable of.
Next, define the job. Beam comes with tried-and-tested workflows that can be assigned to the agent via Task Templates. These are end-to-end automated task steps that the agent can perform effortlessly, once assigned.
As an integral member of your team, you can provide your agent with distinct Personality traits that affect the way it engages with other team members, agents and customers, making it ‘human-like’ in every way!
A Trigger sets an agent in motion, and you get to choose what triggers a certain AI agent into action. Define a webhook or personal API key and take complete control of every active workflow automation.
Supervise your new workforce from an intuitive and responsive tasklist. Once you’ve added to the Agent Database and assigned Tools, Task Templates and a Personality, your agent is ready to begin executing tasks.
RPA
Planning
Planning is done by humans for all tasks at the start
If the flows do not work for a specific use case, the task fails
Complex flowcharts need to be built manually to model processes and their edge cases
If a flow does not work for certain use cases, a human needs to optimize them
Execution
Each step can only produce a set of known outputs
If a task inputs are not as expected, the execution fails
Most executions are trigger based
APA
Planning
Planning is done flexibly according to the requirements of the input query
If the agent gets stuck, it can re-plan
Agent can be taught using text documents (SOP,
Restrictions, and Guidelines)
Agent learns through it's mistakes and user feedback
Execution
Uses knowledge from the database and learns from previous executions
Agent can ask for feedback if it feels unsure
Flows get automatically debugged if it gets unexpected user inputs or certain step fails
Tasks can be executed through multiple interfaces (SDKs, Dashboard, Audio, Chat, Generative UI)|