The semiconductor industry is experiencing a paradigm shift with the emergence of agentic AI—and it’s not just another buzzword. This new approach to artificial intelligence is fundamentally changing how we tackle complex manufacturing challenges, from yield optimization to predictive analytics.
What Makes Agentic AI Different?
Traditional RAG-based AI systems respond to queries and provide information. Agentic AI systems, however, take action. They perform tasks, run code, call APIs, manage state, and iterate through feedback loops. Think of it as the difference between having a research assistant who can answer questions versus one who can actually execute projects on your behalf.
At its core, an AI agent is a system where large language models (LLMs) dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. But the real power emerges when multiple agents collaborate—what we call agentic AI systems.
Key Features That Set Agents Apart:
Individual AI Agents:
- Task planning and decomposition
- Execution pipelines with memory (both short and long-term)
- File and API access capabilities
- Tools integration (ReAct, AutoGen, CrewAI)
Agentic AI Systems (Multi-Agent):
- Collaboration and task delegation between agents
- Modular roles and hierarchies
- Goal-driven planning with shared episodic memory
- Standardized communication protocols like MCP (Model Context Protocol)
- Long-term memory synchronization and adaptive evolution
Real-World Applications in Semiconductor Manufacturing
Natural Language Data Analytics
One of the most immediate applications we’re seeing is the ability to interact with complex manufacturing data through simple conversation. Engineers can now ask questions like “Show me yield patterns for lot 12345” or “Generate a predictive binning pipeline using parametric test data” and have the system automatically:
- Extract relevant information from the request
- Identify the appropriate analytical tools
- Look up necessary parameters
- Construct the required data queries
- Generate comprehensive analysis and visualizations
Automated ML Pipeline Generation
Agentic workflows are revolutionizing how we build machine learning pipelines for semiconductor manufacturing. Instead of manually configuring complex workflows, engineers can describe their objectives in natural language, and the system will:
- Identify appropriate target variables
- Select relevant input data sources
- Create workflows automatically with built-in flexibility and scalability
- Apply ModelOps best practices throughout the process
So now we are blurring the line between what we defined as agentic workflows that are entirely within the context of an LLM-based agentic AI system and the construction of our own workflows that perform some more traditional ML tasks.
Intelligent Yield Diagnosis
Perhaps most impressively, agentic AI can enable automated diagnosis of yield issues by coordinating multiple specialized agents:
- Wafer Sort Analysis Agent: Examines low yield patterns, excursions, and bin/wafer patterns against PCM and metadata
- Final Test Analysis Agent: Correlates yield issues with test metadata
- Root Cause Analysis Agent: Uses Monte Carlo methods and heuristics to determine signal confidence in identified causes
- Workflow Generation Agent: Creates YAML-based workflow definitions that can be executed through CLI or Python SDK
The Technology Behind the Magic
YAML-Driven Workflows
Behind the scenes, agents generate YAML files that contain the structure of analytical workflows. These files provide:
- Text representation of complex UI operations
- Block-based architecture with clear connectors
- Systematic creation
- Flexible execution through multiple interfaces
Model Context Protocol (MCP)
Led by Anthropic, MCP provides a standardized way for different agents to communicate—even across vendor boundaries. This protocol:
- Enables secure, controlled interaction between disparate systems
- Reduces security risks like prompt injection
- Restricts file access to defined areas
- Encourages safe, modular integration
In addition to MCP there is A2A that might be better designed for communication between agents. But what these technologies allow for is the ability to collaborate across disparate systems, so we can start to imagine how agents can collaborate across vendors and between customers and vendors in a standardized language commonly spoken between all agents.
Cutting Through the Hype
Is agentic AI revolutionary or just repackaged existing tools? The honest answer is both. While the concept of connecting applications to accomplish complex tasks isn’t new, the breakthrough lies in LLM technologies that enable truly dynamic, adaptive workflows.
We’ve seen this firsthand in our development work. Using out-of-the-box agentic AI systems, our team successfully wrote Scala code with minimal prior experience. Was it perfect? No. Did it get us 80% of the way there? Probably. Did it require guidance and understanding of the tool? Absolutely.
The key insight: there is no free lunch. Agentic AI is powerful, but it requires proper guardrails, domain expertise, and careful implementation.
Looking Forward: Cross-Industry Collaboration
The most exciting potential lies in agents collaborating across industry boundaries. Imagine semiconductor manufacturing agents working with supply chain agents, quality control agents, and even customer demand forecasting agents to optimize the entire value chain.
This vision requires:
- Scalable and user-friendly interfaces that don’t require PhD-level expertise
- Security and privacy protections for sensitive manufacturing data
- Technology and vendor agnostic approaches that prevent lock-in
- Standardized protocols like MCP that enable seamless integration
If the right guardrails are put in place where individual agents can protect the IP of their respective owners then we can start to imagine how these agents can break down barriers that hinder us today and truly transform the industry.
Key Takeaways for Industry Leaders
- Paradigm Shift: Agentic AI represents a fundamental change in how we apply AI systems, from passive responders to active workflow participants.
- Collaborative Intelligence: The real power emerges when multiple agents work together to solve complex, multi-step problems that were previously out of reach.
- Custom Workflow Creation: Agentic AI excels at building tailored workflows for specific manufacturing challenges, with built-in learning and improvement capabilities.
- Cross-Boundary Solutions: The future lies in agents collaborating across company and industry boundaries to tackle our biggest challenges.
The Bottom Line
Agentic AI isn’t just another AI trend—it’s a fundamental shift toward truly autonomous, collaborative intelligence. For semiconductor manufacturing, this means faster time-to-insight, more sophisticated analysis capabilities, and the potential to solve yield and quality challenges that have long plagued the industry.
The technology is here, standards are emerging, and early adopters are already seeing results. The question isn’t whether agentic AI will transform semiconductor manufacturing, it’s how quickly your organization will adapt to leverage its capabilities.
This post is based on insights from PDF Solutions’ ongoing research and development in agentic AI applications for semiconductor manufacturing. Special thanks to co-authors Tomonori Honda, Anya Jasthi, Shriram Sunder, and Nika Ptushkina for their contributions to this work.