Agent OS
The First Self-Evolving Execution Engine for AI agents
Agent OS is Beam's proprietary framework for production AI agents. Unlike static automation, Beam agents learn from every interaction. Improving accuracy automatically without manual maintenance. Graph-based execution combines workflow reliability with AI flexibility. Multi-agent orchestration scales to enterprise complexity. The result is agents that get better every day, not agents that stay broken for weeks.
Agent OS
Core capabilities for production AI agents
Everything you need to build, deploy, and improve AI agents at enterprise scale. From graph-based execution to self-learning, these capabilities make Beam agents production-ready.
Reliable Graph-based execution
Reliability meets flexibility. Fixed workflows for predictable steps, AI reasoning for complex decisions. Both in the same agent. No more choosing between automation and intelligence.
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1500+ skills & actions ready to use
1500+ pre-built actions. Custom AI tools for anything else. Connect to SAP, Salesforce, Oracle, or build your own. Every action your workflow needs, ready to use.
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Agents that learn from every interaction
This is what makes Beam different. Agents learn from failures automatically. 5% accuracy to 100% in 30 seconds. No manual prompt engineering. VW chose us for this.
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Built-in accuracy tracking
Configure evaluation criteria per node, and the system validates every output automatically. See accuracy scores at workflow, step, and variable level. When scores drop below threshold, auto-retry kicks in. Track completion rate, evaluation score, and feedback score over time.
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Humans in control when it matters
Humans stay in control of high-stakes decisions. Agents prepare everything, you approve. Start with full oversight, reduce as trust builds. Risk managed, efficiency gained.
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Any LLM, no lock-in
Use any LLM. GPT, Claude, Gemini, Llama, your fine-tuned models. Benchmark to find the best for your use case. Switch without rebuilding. No vendor lock-in.
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Persistent memory across tasks
Four memory types—short-term for current tasks, long-term for persistent knowledge, working for active processing, episodic for past interactions. Upload PDFs, CSVs, and documents to agent memory. Vector embeddings enable semantic search across your content.
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Graph-based execution for reliability and flexibility
A flow is a graph-based structure that defines agent execution. Nodes perform actions. AI processing, API calls, data operations. Branches create conditional paths. Merging points converge multiple branches. The result is you get the reliability of fixed workflows for predictable steps, and the flexibility of AI reasoning for complex decisions. In the same agent, in the same workflow.
Nodes: AI processing, integrations, data operations
Branches: Conditional paths with edge selection criteria
Merging: Multiple branches converge into single execution path
Unified building blocks for every agent
Agent OS brings skills, integrations, tools, triggers, and graphs together in one system. Triggers start work from events (webhooks, schedules, emails, app actions). Skills encapsulate repeatable logic you can reuse across agents. Tools wrap LLM capabilities and custom logic. Integrations connect to SAP, Salesforce, Oracle, Workday, and your internal systems. Graphs orchestrate everything into end-to-end workflows. Instead of scattered scripts and one-off bots, you get a single, coherent execution model.
Triggers: Webhook, schedule, email, and app events to start agents
Skills: Reusable bundles of logic and prompts shared across agents
Integrations & tools: 1500+ connectors plus custom tools in one catalog
Multi-agent orchestration to scale without chaos
Multi-agent collaboration allows one agent to trigger another within a workflow. Build specialized agents that excel at specific tasks. Invoice processing, candidate screening, compliance checking. Coordinate them through a central orchestrator. MCP (Model Context Protocol) enables communication with external agent platforms (deployed with IBM and Cisco). A2A protocol support for flexible integration patterns.
Specialized agents for specific domains
Reusability across multiple workflows
MCP and A2A protocol for external systems
Self-learning for agents that improve automatically
The Learning Hub tracks tool performance across all workflow nodes, identifying underperforming tools below accuracy thresholds. When outputs fail, mark what went wrong. AI analyzes failures, identifies patterns, and rewrites prompts with clearer instructions. Validation testing automatically retests against failed cases before deployment. Transform 5% accuracy to 100% in about 30 seconds.
Automatic prompt rewriting from failure patterns
Learns domain expertise automatically (libraries, formulas, industry conventions)
Self-corrects when it learns wrong behaviors
Evaluation framework to know exactly how good you are
Configure evaluation criteria per node. The system validates format, required fields, and data correctness automatically. Every execution gets an accuracy score. Track completion rate (95%+ target), evaluation score, and feedback score. Auto-retry triggers when scores drop, with self-healing prompts. You always know exactly how your agents are performing. And you can prove it.
Automatic output validation against criteria
Node-level accuracy scoring
Auto-run self-healing on low scores
Human-in-the-loop for control without bottlenecks
Three automation modes control agent autonomy. Fully autonomous means end-to-end without human intervention. Human-in-the-loop pauses at designated checkpoints for review. Consent nodes show execution context and proposed action, humans approve or reject. Hybrid means autonomous execution with selective oversight at critical steps. All pending tasks route to a centralized Inbox.
Consent nodes pause for approval
Centralized Inbox for all pending approvals
Gradual autonomy as trust builds
Model flexibility to use any LLM and switch anytime
Agent OS is model-agnostic. Use OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), Meta (Llama), or your custom fine-tuned models. Specify endpoint, API key, and model version. Beam routes requests. Benchmark different LLMs for your specific use case. Some tasks need GPT-4, others work fine with Llama. For on-premise, bring your own models. Data sent to LLMs is not used for training.
GPT, Claude, Gemini, Llama, custom models
Benchmarking to select optimal model per use case
On-premise deployment with your own models
Memory system so agents remember what matters
Four memory types serve different needs. Short-term stores current task context. Long-term stores persistent knowledge accumulated over time. Working handles active processing and temporary data. Episodic stores sequences of events and past interactions. Memory uses vector embeddings for semantic search. Upload files (PDF, CSV, TXT, JSON) to agent memory. Content becomes accessible to all nodes automatically.
Four memory types with different retention
Vector embeddings for semantic retrieval
File upload (PDF, CSV, TXT, JSON)