Leading Platform for Agentic Automation & AI Agents

What is Beam AI?

Beam AI is an intelligent automation platform designed for enterprise operational processes, operated by Beam AI Inc. It brings together the creation of AI agents, process visualization, various tools, memory functions, enterprise integration, evaluation, monitoring, and deployment within a single workspace.

The old name in the database was merely a marketing slogan; the actual name is Beam AI. It is designed to handle complex processes such as those related to finance, human resources, customer service, sales operations, and document processing, rather than simply providing a chatbot.

Core competencies

• Quickly create common business agents using templates.
• Describe the requirements in natural language and generate an editable workflow.
• Configure nodes, branches, loops, and merges on the visualization canvas.
• Set goals, roles, tools, memory, and upgrade paths for the agent.
• It connects more than 1,500 enterprise software and data services.
• Combine deterministic steps with AI reasoning within the same workflow.
• Pause before performing sensitive actions and request manual approval.
• Automatic evaluation and retry are set for nodes and the entire process.
• Organize multiple specialized agents to work together to handle complex tasks.
• Create, manage, and execute proxy tasks through APIs.
• Choose SaaS, dedicated instances, self-hosted, or on-premises deployment.

Three ways to create proxies

Visual workflow

Each agent in Beam operates through Flow, which is a graph composed of nodes, connections, conditions, and endpoints. Teams can define the steps that must be carried out strictly, and they can also incorporate model inference when it’s necessary to process unstructured input.

Tools and variables

A process node is typically assigned a tool, which receives the variables from previous nodes and outputs data that can be used by subsequent nodes. The parameters can come from AI-generated values, fixed values, user input during execution, proxy memory, or the results of the previous node.

• System tools are responsible for files, databases, and basic communication operations.
• The integration tool invokes the configured enterprise service actions.
• Custom GPT tools are used for classification, extraction, decision-making, and content generation.
• Custom integrations connect to internal APIs, legacy systems, and proprietary services.
• Structured output helps subsequent nodes to read fields stably.
• Multiple connections allow the same service to use production, testing, or customer accounts separately.

Over 1,500 integrations

Beam offers pre-built connectors for CRM, email, office applications, documents, storage, ticketing systems, as well as business and development tools. The platform handles common tasks such as authentication, rate limiting, error handling, and retry mechanisms; users still need to select the appropriate account and grant only the minimum necessary permissions.

Self-learning and evaluation

Beam enables the setting of evaluation criteria for entire workflows, individual steps, and key variables. If the output falls below a certain threshold, automatic retries can be carried out; the results that have been manually corrected can also be included in the test set, thereby helping to identify failure patterns and improve the agent over time.

• Define business correctness standards using natural language.
• Calculate an accuracy score ranging from 0 to 100 for each node.
• Automatic retry or a backup path is triggered when the value is below the threshold.
• Save the results of manual correction as the desired output.
• Run test data in batches and perform regression comparisons.
• View the completion rate, average score, and performance changes.
• Run the failed historical tasks again after modifying the rules.

Manual approval

For payments, high-value invoices, contract modifications, customer upgrades, and external communications, the process can be paused via the Consent node, with pending tasks being sent to a unified Inbox. The team can request approval for each case first, and then gradually increase the level of autonomy granted to agents based on stable performance data.

Multi-agent orchestration

Complex processes can be divided among multiple specialized agents: one agent is responsible for reading invoices, another for verifying compliance, yet another for handling the approval process, and a different agent is tasked with entering the data into the ERP system. A coordination agent is in charge of transmitting status updates, managing dependencies, and compiling the results.

• Define clear responsibilities and input/output boundaries for each agent.
• Prevent multiple agents from modifying the same business record at the same time.
• Tools and tasks are exchanged with external proxy platforms via MCP.
• Set a structured format for data transmitted across proxies.
• Design compensation actions for timeouts, failures, and repeated executions.
• Maintain complete tracking to identify the responsible nodes.

Typical business scenarios

Deployment method

API capabilities

Beam offers a formal API that enables authentication using keys and workspace identifiers. Developers can programmatically create, manage, and execute proxy tasks, and integrate Beam processes into existing applications, event systems, or backend services.

• Use API keys to authenticate requests.
• Specify the current workspace in the request to maintain isolation between different organizations.
• Create a proxy task and check its execution status.
• The process is triggered via Webhook or business events.
• Write the results of structured tasks back to one’s own system.
• Different keys and workspaces are used for the production and testing environments.
• Set rules for key rotation, revocation, and minimum usage scope.

API integration steps

1. Create a Beam workspace and verify the API access permissions.
2. Generate a dedicated API key in the account; do not include it in the client code.
3. Record the identifier of the target workspace and configure server-side secret management.
4. First, verify the identity, input format, and return structure through test tasks.
5. Create or select a proxy, and prepare the task input fields.
6. After submitting a task, poll the status or receive event callbacks.
7. Handle timeouts, duplicate submissions, rate limiting, and retry on failures.
8. Record the task identifier, version, cost, and final business outcome.

Complete deployment process

1. Choose processes that are high-frequency, have clear rules, and yield measurable results.
2. Organize SOPs, examples, exceptions, approval thresholds, and compliance requirements.
3. Create the first version of the agent from a template, dialogue generation, or a blank canvas.
4. Connect to the test account and restrict it to read-only operations or low-risk actions.
5. Define inputs, outputs, evaluation, and failure handling for the node.
6. Use historical tasks to create normal, abnormal, and boundary test sets.
7. In the initial stage, all key actions required manual approval.
8. Compare accuracy, cost, and processing time in small-batch production tasks.
9. The correction failed, and the scope of automatic execution was gradually expanded.
10. Continuously monitor quality, tokens, permissions, business outcomes, and data retention.

Prices and billing

The price information was verified on August 23, 2026; the actual amounts, taxes, exchange rates, and discounts may vary, and the final figures will be those displayed on the settlement page.

Beam adopts an enterprise-based pricing model that takes into account usage levels and customized solutions; there are no fixed packages available for all scenarios. The reference figure provided on the order processing page is around $499 per month, which covers approximately 2,000 orders, while the cost of custom AI agent implementation services starts at $10,000.

Items whose price needs to be confirmed

• How is each task or order counted?
• Whether model tokens are included in the platform price.
• Whether failure, retry, evaluation, and manual return are charged.
• Are there any additional limits on integrated connectors, APIs, and Webhooks?
• Custom proxy settings specify the number of processes and modification rounds.
• Additional costs for dedicated instances, on-premises deployment, and regional hosting.
• Minimum contract amount, unit price for excess usage, support levels, and exit fees.
• The method for adjusting prices after transitioning to production on a pilot basis.

Safety and compliance

Beam offers enterprises role-based permissions, approval processes, audit tracking, layered security measures, as well as various deployment options. It boasts compliance with SOC 2 Type II, ISO 27001, and GDPR standards, and supports hosting in the European Union, private instances, and on-premises deployment.

Privacy and data processing

Beam processes account, business, usage, and technical information based on website visits, platform usage, and participation in sales or events. The privacy policy emphasizes customers’ control over the lifecycle of their data; nevertheless, companies should still specify in contracts the rules regarding data deletion, retention, use by subcontractors, cross-border transfers, and modeling.

• Upload only the minimum amount of data required to complete the process.
• Create separate connections for production, testing, and different customers.
• Mask personal information and keys in the logs and evaluation sets.
• The scope of the contract, which ensures that customer data will not be used to train public models.
• Define the retention periods for task records, outputs, attachments, and tracking data.
• Establish processes for access, correction, export, and deletion requests.
• Export proxy, process, evaluation, and audit materials before exiting the service.

Workspace and team management

The workspace is used to isolate agents, tasks, connections, and settings belonging to different organizations, and it supports roles such as administrators, editors, and viewers. Shared resources facilitate teamwork, but agents, keys, and production connections should not be available to all members by default.

• Separate development, testing, and production tasks.
• Grant administrator, editor, and read-only permissions based on responsibilities.
• Use different connections to distinguish between departments, customers, and environments.
• Approval is required for the release of proxies and for any key modifications.
• Regularly check for unused proxies, keys, and connections.

GitHub and the open-source status

No official open-source repository for the core code of the Beam AI commercial platform has been identified; therefore, the product should be classified as a closed-source enterprise service. The availability of public APIs and development documentation does not mean that Agent OS, Studio, or the cloud platform agents are open source.

Which organizations are suitable?

• Companies that wish to automate the processing of invoices, orders, and shared service tasks.
• Operational teams that need to combine AI-based reasoning with deterministic approval rules.
• Organizations with a large number of SaaS, ERP, CRM systems, as well as the need to connect to legacy systems.
• Regulated industries that require manual approval, audit tracking, and quality assessment.
• It is hoped that business professionals can be included in the automation process through a no-code interface.
• Companies that need the collaboration of multiple specialized agents to carry out end-to-end processes.
• Technical teams that require APIs, private instances, or on-premises deployment.

Product advantages

• It covers the entire lifecycle of proxy creation, operation, evaluation, monitoring, and deployment.
• Templates, dialogues, and custom canvases are adapted to different levels of skill.
• More than 1,500 integrations reduce the work associated with connecting common systems.
• Deterministic nodes and AI reasoning can be incorporated into the same process.
• Node-level evaluation and automatic retry support continuous improvement.
• The Consent node allows human decision-making to remain in place for high-risk actions.
• It supports multi-agent, MCP, API, and custom internal integrations.
• The deployment scope covers SaaS, dedicated infrastructure, and on-premises environments.

Restrictions and Precautions

• There is no unified fixed package or publicly available total price that applies to all businesses.
• $
• Complex processes still require SOP development, integration, testing, and change management.
• Agent self-learning cannot replace the manual establishment of correct business standards.
• There may be differences in the performance and stability of more than 1,500 types of connectors.
• Automatic retries may increase the costs associated with models, tasks, and external systems.
• Agents with high permissions pose risks of data miswriting, duplicate executions, and data leakage.
• Closed-source platforms can generate costs related to process, data, and integration migration.

Pre-purchase checklist

1. Select a pilot process with clear volume of activity and cost metrics.
2. Provides genuine SOPs, exception cases, historical data, and approval rules.
3. A demonstration of the complete execution chain, from triggering to system write-back, is required.
4. Verify the costs associated with platform agents, implementation, models, integration, and deployment.
5. Confirm the task count, failure retry settings, and overage billing definitions.
6. Check the specific actions, permissions, and error handling for the required connectors.
7. Request information on the scope of security certification, subcontractors, and data processing agreements.
8. Test hints include injection, repeated execution, privilege escalation, and manual takeover.
9. Agree on the methods for data export, process migration, deletion, and contract termination.
10. Expansion is determined by accuracy, processing time, labor rate, and total cost.

Frequently Asked Questions

Is Beam AI a no-code platform?

It supports templates, natural language generation, and visualized workflows, allowing business users to carry out many configurations without writing code. However, complex integrations, APIs, data schemas, security aspects, and production deployment still require the involvement of engineers.

How much does Beam AI cost?

The reference pricing for order processing starts at $499 per month, covering around 2,000 orders; custom proxy settings cost starting from $10,000. For other processes, pricing is based on usage or customized quotes, and it is necessary to check the total cost at the time of purchase.

Is an API provided?

It provides API keys and the identifier of the current workspace for authentication, enabling the creation, management, and execution of proxy tasks. For production use, the keys must be stored on the server side, and mechanisms for rate limiting, retry handling, and idempotency must be in place.

Can it be deployed locally?

You can choose between SaaS, dedicated hosting, self-hosting, on-premises, or hybrid deployment. The specific responsibilities regarding infrastructure, methods of upgrading, model integration, and support costs must be specified in the enterprise contract.

Will Beam AI approve payments entirely automatically?

It is possible to configure automatic or manual approval according to the established process, but high-value transactions and sensitive operations are better handled by using the Consent node for suspension. Companies need to define their own approval roles, thresholds, dual-control mechanisms, and audit rules.

Is Beam AI open source?

Business platforms are not open-source products; there is no official public repository for the core platform. Public APIs, documentation, and the option of self-hosting cannot be considered as an open source code.

Summary

Beam AI is suitable for companies that wish to transform complex operational SOPs into functional intelligent agent processes. It offers capabilities such as no-code creation, extensive integration options, manual approval processes, automatic evaluation, multi-agent orchestration, and various deployment methods. The value of this platform lies in the fact that it enables AI to not only generate suggestions but also to utilize tools within controlled processes and integrate those results back into business systems.

The effectiveness of implementation depends on the clarity of the processes, data quality, access rights, evaluation criteria, and the design for human intervention. The most prudent approach is to start with a single, high-frequency process, maintaining a high level of approval at first; only afterward should it be decided whether to increase the degree of autonomy based on the actual failure rate and total costs.