For decades, progress in chip design has been defined by automation.
Electronic Design Automation (EDA) tools evolved to handle increasing complexity by accelerating individual tasks such as faster simulation, better synthesis, and more scalable verification. The underlying assumption was clear. If each step in the design flow could be optimized, overall productivity would follow.
That assumption is now being challenged.
As semiconductor and PCB systems grow exponentially more complex, the bottleneck is no longer only within individual tools. It lies between them, in the coordination, interpretation, and iteration required across the design lifecycle. The shift underway is subtle but profound. It is a move from automation to collaboration.
And increasingly, that collaboration includes AI.
The limits of step-wise automation
Traditional EDA automation operates within defined boundaries. Tools are configured, executed, and validated in sequence. Each stage produces outputs that feed into the next, with engineers orchestrating the flow through scripts, configurations, and manual oversight.
This model worked well when workflows were relatively stable and toolchains were more contained. But modern chip design no longer fits that paradigm.
Today’s semiconductor systems involve billions of transistors, heterogeneous architectures, and tightly coupled hardware-software interactions. PCB design adds another layer of complexity by integrating signal integrity, thermal constraints, and manufacturing considerations. The result is a highly fragmented, multi-domain workflow where dependencies are constant and iteration is continuous.
In such an environment, optimizing individual tools delivers diminishing returns. Engineers spend more time managing transitions, translating intent across tools, debugging inconsistencies, and coordinating across teams, than executing core design tasks.
This is where automation reaches its limit.
The rise of orchestration
The pursuit of local optimas is moving to the pursuit of end-to-end optimas. The next phase of EDA evolution is not about making tools faster. It is about making workflows smarter.
Agentic AI introduces a new paradigm called orchestration.
Unlike traditional automation, which focuses on executing predefined tasks, agentic systems operate across the entire design lifecycle. They can interpret high-level intent, plan multi-step workflows, invoke the right tools, and adapt dynamically as conditions change.
In practice, this means moving from isolated point solutions to a unified intelligence layer that spans front-end design, verification, physical implementation, PCB layout, and manufacturing sign-off.
For example, instead of manually configuring multiple tools to validate a design change, an AI agent can:
- Understand the design intent expressed in natural language
- Identify affected components across RTL, verification, and layout
- Execute simulations and analyses across relevant tools
- Correlate outputs from diverse data formats such as waveforms, netlists, and logs
- Surface actionable insights, including likely root causes and optimization paths
Crucially, this orchestration is not ad hoc. It is grounded in structured, domain-specific frameworks that use specialized parsers, validated workflows, and embedded guardrails to ensure outputs meet production standards.
The result is not just faster execution, but a more cohesive and collaborative design process.
From tools to ecosystems
This shift also reflects a deeper reality of modern EDA. It is inherently multi-vendor and ecosystem-driven.
No single tool or platform can cover the entire semiconductor and PCB design lifecycle. Engineers routinely work across diverse toolchains, each optimized for specific tasks. While this diversity enables specialization, it also introduces fragmentation.
Agentic AI addresses this challenge by acting as a unifying layer across tools.
Through centralized data architectures, often built around multimodal data lakes, and retrieval-augmented frameworks trained on domain-specific knowledge, AI systems can maintain a consistent understanding of design context across workflows. This prevents the loss of context that often occurs when moving between tools and reduces the risk of errors or inefficiencies.
Equally important is scalability. As workflows expand, traditional approaches struggle with what can be described as context saturation, where the volume of data and interactions overwhelms both tools and engineers. By modularizing workflows into reusable agent skills and orchestrating them dynamically, agentic systems enable deterministic execution at scale.
In this model, collaboration is no longer just between engineers. It is between engineers and a network of intelligent agents operating across the design ecosystem.
The role of the engineer
Despite these advances, the role of the engineer remains central.
Chip design is not a purely deterministic process. It involves complex trade-offs across power, performance, and area, often under conditions of uncertainty. Decisions around design intent, risk tolerance, and sign-off require human judgment.
Agentic AI does not replace this expertise. It amplifies it.
By handling execution, coordination, and analysis, AI allows engineers to focus on higher-value tasks such as defining architecture, evaluating trade-offs, and ensuring design integrity. Human-in-the-loop checkpoints ensure that critical decisions remain under engineering control, preserving accountability and trust.
Over time, this dynamic is likely to evolve further. As AI agents become more capable, engineers will increasingly act as supervisors of complex, multi-agent systems, guiding strategy rather than executing individual tasks. In essence, every engineer is now a manager of several agents and is responsible for the collective outcome.
India’s strategic moment in chip design
This transformation is particularly relevant for India.
India already plays a critical role in the global semiconductor ecosystem, with a significant share of the world’s chip design talent. The design talent has not only grown in numbers but has evolved into expertise powerhouse. Global companies rely on Indian engineering teams for everything from design definition, creation verification, implementation, validation, manufacturing readiness to system integration.
At the same time, the government’s India Semiconductor Mission is accelerating efforts to build a more complete semiconductor value chain, spanning design, manufacturing, and packaging. Combined with the rapid expansion of Global Capability Centres, this positions India as both a talent hub and a strategic innovation center.
However, as responsibilities expand from design support to end-to-end ownership, the complexity of workflows will increase significantly. Managing this complexity through traditional automation alone will not be sustainable.
Agentic orchestration offers a path forward.
By enabling seamless collaboration across tools, teams, and domains, AI can help Indian engineering organizations scale their capabilities without proportionally increasing overhead. It also democratizes access to expertise, allowing less experienced engineers to operate effectively within complex workflows by leveraging AI-driven guidance.
Additionally, India is witnessing a rapid expansion in semiconductor design startups. By construction the startups need to be more nimble, more efficient and create successes quickly.
In a global industry defined by speed and precision, this can be a decisive advantage.
Looking ahead
The evolution from automation to collaboration marks a turning point in chip design.
As agentic AI systems mature, they will move beyond reactive task execution to proactive design assistance, anticipating issues, optimizing workflows, and coordinating across domains in real time. Multi-agent systems could eventually manage entire phases of the design lifecycle, operating at a scale and speed that would be impossible for human teams alone.
But the core principle will remain the same. Collaboration.
Not just between engineers, but between humans and intelligent systems working together to solve increasingly complex problems.
For the semiconductor and PCB industry, this is more than a technological shift. It is a redefinition of how design happens.
And for India, it is an opportunity to lead, not just in talent, but in shaping the future of intelligent, collaborative engineering.