As AI agents become commonplace in enterprise workflows, teams are discovering the limitations of building task-specific automated systems from scratch. Large Action Models (LAMs) represent the foundational layer that transforms how we build agents—providing the general-purpose perception, planning, and execution capabilities that individual agents can leverage rather than reinvent. Instead of building isolated automation tools, LAMs enable teams to create more capable, adaptable agentic systems that can operate across diverse contexts and applications.
LAMs represent a shift in AI from passive content generation to active task execution. Unlike Large Language Models (LLMs) that excel at text generation and understanding, or Visual Language Models (VLMs) that combine text and visual processing, LAMs are designed to autonomously perceive, plan, and execute multi-step actions within digital and physical environments. While AI agents are able to perform specific automated tasks, LAMs serve as foundational architecture that enable more general-purpose, language-driven agentic systems capable of operating across diverse contexts and applications.
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The core distinction lies in their operational approach. While LLMs can describe flight booking and VLMs can analyze booking screenshots, LAMs actually navigate websites and complete reservations. That said, current implementations work best in controlled environments. Many LAMs achieve this capability by pairing neural perception modules with symbolic planners in a neuro‑symbolic architecture, though some recent systems rely on a single end‑to‑end neural network instead.
Recent developments have validated this potential. OpenAI’s ChatGPT agent, launched in July 2025, represents the first major production deployment of a unified LAM system. By combining web browsing capabilities, deep research functionality, and terminal access within a single model, ChatGPT agent demonstrates how LAMs can move beyond controlled environments to handle complex, multi-step workflows across diverse applications. The system achieved state-of-the-art performance on benchmarks like Humanity’s Last Exam (41.6% accuracy) and FrontierMath (27.4% accuracy), while maintaining the safety controls necessary for enterprise deployment.
In the case of ChatGPT agent the underlying large action model is not separately exposed; OpenAI surfaces it as a managed service with safety guardrails. Purists might say the “LAM” is the model inside the service, while “ChatGPT Agent” is a LAM-powered agent.
A Spectrum of LAM Use Cases
Large Action Models are transitioning from concept to reality, tackling complex, multi-step sequences of actions once exclusively performed by humans. In the consumer sphere, this technology is emerging in mobile integrations like Google Gemini Live, which organize personal data across applications, and personal assistants like the Motorola LAM or Rabbit R1, which handle tasks like ordering meals or booking rides. However, early implementations show mixed real-world results.
This same power is being applied to streamline business operations. Within the enterprise, ServiceNow agents automate internal IT and HR workflows, while specialized tools like 11x’s “Alice” execute external-facing tasks like prospect research and sales outreach. Similarly, specialized agents like Shortcut are emerging to automate complex knowledge work within specific applications, such as performing multi-step data modeling and analysis in Microsoft Excel.
The release of ChatGPT agent marks a significant milestone in LAM maturity, offering the first widely-available unified system that consolidates multiple capabilities. Unlike earlier specialized tools, ChatGPT agent integrates visual web browsing, text-based research, terminal access, and API connectivity within a single model. This architectural approach enables seamless transitions between different interaction modes—gathering calendar information through an API, analyzing web content via text processing, and completing transactions through visual interface manipulation.
For development teams, this represents a shift from integrating multiple specialized agents to leveraging a foundational LAM that can adapt its approach based on task requirements. The system’s ability to generate editable artifacts (presentations, spreadsheets, code) while maintaining context across tool switches demonstrates the practical value of unified LAM architectures over tool-chaining approaches.
The application of LAMs extends into highly specialized and regulated fields. In software engineering, AI developers like Cognition Devin attempt to independently write, test, and debug code, while frameworks like Microsoft AutoDev coordinate teams of agents on complex programming projects. In data-intensive sectors such as healthcare and finance, these models reduce administrative burdens by managing patient scheduling and insurance claims, or enhance security and compliance by performing real-time fraud analysis and automating regulatory filings. From controlling industrial robots on a manufacturing floor to navigating websites and desktop applications, LAMs provide the foundational capability for a new era of digital and physical automation.
Navigating the Large Action Model Landscape
The LAM landscape has crystallized around production viability, with ChatGPT agent establishing a new benchmark for unified agentic systems. OpenAI’s decision to sunset the standalone Operator tool in favor of the integrated agent approach signals industry convergence toward comprehensive LAM platforms rather than specialized tools.
For enterprise teams evaluating LAM adoption, this consolidation simplifies the decision matrix. Instead of choosing between separate browsing, research, and automation tools, teams can now leverage unified systems that handle multi-modal interactions. The performance metrics from ChatGPT agent—including 45.5% accuracy on spreadsheet tasks and 68.9% on web research benchmarks—provide concrete baselines for capability assessment.
Industry Reactions: Promise, Skepticism, and Pragmatic Adoption
After studying teams evaluating Large Action Models, I’m seeing a split in perspective. Some enterprise teams seem genuinely excited about the productivity gains they’re seeing—particularly in workflow automation where LAMs can handle those tedious multi-step processes that eat up developer time. But there’s also a healthy skepticism, especially after some high-profile consumer products like the Rabbit R1 stumbled out of the gate. The conversation often turns to whether we’re witnessing a true paradigm shift in autonomy or just a more sophisticated, and perhaps brittle, form of tool-chaining wrapped in new marketing.
The reality is that most LAM implementations today work well in narrow, well-defined scenarios but struggle with the unpredictability of real-world environments. Success stories often come from carefully controlled deployments where the scope of actions is limited and the environment is stable.
The ChatGPT agent launch has shifted industry sentiment from cautious evaluation to slightly more active planning. Early adopters report particular success with knowledge work automation—competitive analysis, financial modeling, and presentation generation—where the agent’s ability to combine research and artifact creation provides immediate value. However, the 400 messages per month limit for Pro users and 40 for other tiers indicates that even production LAMs require usage management as organizations scale adoption.
ChatGPT agent’s integration of safety controls—including explicit user confirmation for consequential actions and ‘Watch Mode’ for critical tasks like email sending—addresses enterprise concerns about autonomous systems. These controls represent a pragmatic approach to LAM deployment that prioritizes user oversight while enabling automation of routine workflows.
As LAMs become more viable, security-conscious organizations will likely mirror their early cloud adoption playbook when approaching LAMs, proceeding with the same caution that defined their initial cloud strategies. The expanded attack surface concerns are real—when you give an AI system the ability to act on your behalf across multiple applications, you’re essentially handing over the keys to your digital kingdom. Meanwhile, the job displacement anxiety is palpable in customer service and administrative roles, though my sense is that teams who frame LAMs as augmentation rather than replacement tend to have much smoother adoption experiences.
Development Priorities for Enterprise-Ready LAMs
So, where do we go from here? ChatGPT agent’s deployment reveals the next phase of LAM development priorities. Usage constraints (40-400 messages monthly) highlight the need for efficiency optimizations that maximize task completion within limited interactions. The system’s functionality, while promising, shows that artifact generation requires significant refinement to match professional standards.
Enterprise adoption will drive requirements for enhanced security controls, audit trails, and compliance frameworks. The system’s current biological risk safeguards and prompt injection protections establish baseline security expectations that future LAMs must meet or exceed.
Implementation Lessons from ChatGPT Agent
Early deployments of ChatGPT agent provide concrete insights for teams planning LAM integration:
- Architecture Decisions: The unified model approach (combining browsing, research, and terminal access) proves more effective than microservice architectures for user experience, despite increased complexity in safety controls and resource management.
- Usage Patterns: Real-world usage gravitates toward knowledge work automation—research synthesis, document generation, and data analysis—rather than transactional web interactions. This suggests LAM implementation should prioritize content creation workflows over e-commerce automation.
- Safety-Performance Trade-offs: The explicit confirmation requirements for consequential actions create friction but enable enterprise adoption. Teams implementing LAMs should plan for approval workflows that balance automation benefits with organizational risk tolerance.
- Integration Strategies: The connector framework (Gmail, GitHub integration) demonstrates how LAMs can extend existing business applications rather than replacing them. This integration-first approach reduces deployment complexity while maximizing organizational value.
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