Modern AI has dazzled the world with large language models (LLMs)that can chat, write, and reason impressively well. However, traditional LLMs have a critical limitation: they respond to one prompt at a time and have no persistence or agency beyond generating text.
At Vaisys, our research lab is working on the next evolution of this technology — the Large Action Model (LAM). We build AI systems that don't justtalk about tasks, but actively perform tasks in a continuous, autonomous manner, all while learning and adapting over time.
An Agentic LAM is an AI agent endowed with three key extensions beyond a standard LLM, addressing the fundamental limitations that prevent current AI from true autonomous operation.
Persistent long-term memory that goes far beyond traditional LLM context windows, enabling true continuity and learning.
Knowledge graphs for structured relationships and facts
Episodic memory logs for past dialogues and experiences
Ability to build upon experiences from yesterday, last week, or last year
Self-managed context that doesn't lose critical information
Primary actuator capabilities that allow agents to act on the world, not just generate text responses.
Execute actions via external APIs, databases, and IoT devices
Dynamic tool discovery using Model Context Protocol (MCP)
Monitor system metrics, send emails, create documents autonomously
Invoke specialized AI models as subroutines for complex tasks
Continuous operation towards objectives with internal reasoning loops that eliminate the need for constant human prompting.
Internal planning, execution, observation, and adjustment cycles
Advanced planning algorithms combined with LLM reasoning
Goal persistence across sessions and time periods
Human consultation only when needed, not for every step
Crucially, Agentic LAMs are designed with comprehensive guardrails so that all this autonomy remains safe and controllable. We incorporate human oversight at every layer, with certain actions requiring human approval and agents alerting humans when encountering uncertainty or anomalies.
Our research is pushing the boundaries of what autonomous AI agents can do, focusing on capabilities that address real enterprise needs while maintaining safety and human control.
24/7 site reliability engineering with self-healing capabilities and intelligent problem resolution.
Continuous monitoring of system logs and performance metrics
Autonomous diagnosis using memory of past incidents and system knowledge
Self-healing through service restarts, rollbacks, and configuration adjustments
Human escalation only for novel or critical issues with complete context
Proactive code maintenance, improvement, and testing without human intervention for routine tasks.
Periodic code review for bugs, inefficiencies, and best practice adherence
Autonomous refactoring and code quality improvements
Automated test generation and execution with results analysis
Pull request creation with detailed change documentation
Continuous data analysis and decision support with autonomous report generation and recommendation systems.
Real-time monitoring of data streams, news feeds, and market indicators
Autonomous analysis and pattern recognition across multiple data sources
Daily briefings and reports generated without human intervention
Risk and opportunity identification with supporting evidence
Single agent architecture capable of learning new roles and domains through modular knowledge integration.
Core intelligence not confined to single tasks or domains
Dynamic role switching through knowledge module loading
Rapid adaptation from one specialty to another with minimal adjustment
Versatile AI assistants that scale across organizational needs
Continuous improvement through experience, feedback, and controlled self-modification of capabilities.
Knowledge base updates based on new scenarios and experiences
Prompt and strategy optimization through performance analysis
Controlled neural network parameter adjustment via reinforcement learning
Effectiveness improvement with each completed task within safety bounds
Creating Agentic LAMs is cutting-edge work that comes with significant technical and ethical challenges. We are actively working on solving these fundamental problems responsibly.
Ensuring autonomous agents remain aligned with human intent and ethical norms across hundreds of daily decisions.
Rigorous simulation testing in controlled environments
Constraint-based rules with explicit behavioral boundaries
Real-time oversight with supervisor processes for risk detection
Human feedback loops in training for complex decision scenarios
Optimizing long-term knowledge storage and retrieval to prevent information overload while maintaining context.
Hybrid symbolic knowledge graphs for structured relationships
Vector-based episodic memory for temporal experience storage
Intelligent summarization and distillation to prevent knowledge bloat
Dynamic memory prioritization based on relevance and recency
Maintaining cost-effective operation for continuously running agents without infinite compute consumption.
Specialized smaller models for routine tasks with LLM escalation
Intelligent idle modes during periods of low activity
Dynamic resource allocation based on task complexity
Efficient caching and model distillation techniques
Developing new measurement frameworks for autonomous agent performance beyond traditional static benchmarks.
"Hours of work saved" and "tasks completed safely" metrics
Qualitative and quantitative performance tracking over time
Cross-scenario reliability and adaptability measurements
Transparency reporting for user trust and system optimization
We push this frontier responsibly. We publish key findings, collaborate with academic and industry partners, and run internal "red team" drills to think through potential misuse scenarios. Agentic AI has immense potential, but it must be approached with diligence and humility.
Agentic LAM is more than a research project for us — it's a vision of how AI can truly become a partner in enterprise settings. Much of what we learn here flows downstream into our products. Today's early LAM prototypes might become tomorrow's ultra-capable AI Workers in our product lineup.
We believe the shift from passive language models to active, large action models will define the next decade of AI advancement. Vaisys is proud to be at the forefront of this shift, ensuring that as AI agents become more autonomous, they also become more valuable, reliable, and aligned.