Roadmap: Phase 2 - Walk

This report provides a comprehensive, evidence-based analysis of the Project Chimera Phase 2 strategic plan, benchmarking its technical, organizational, and financial ambitions against real-world industry practices. The analysis confirms that the technical pillars of Project Chimera—specifically the deployment of Reinforcement Learning (RL) for Power, Performance, and Area (PPA) optimization and the use of AI for advanced verification—are not only viable but are also strategically aligned with the proven, state-of-the-art direction of the semiconductor industry.

The true innovation, and therefore the primary execution risk, lies not in the individual AI components but in the ambitious goal of integrating them into a unified, end-to-end multi-agent system (MAS) orchestrated by a central MCP Server. This integrated architecture offers the potential for a profound competitive advantage by solving the critical problem of context loss between siloed design stages.

To translate this vision into action, this report now includes a detailed Phase 2 Execution Plan, providing a manager-level, step-by-step guide for implementation. It outlines the tactical roadmap, including an expanded plan for the People & Culture workstream, defines the new roles and responsibilities for each engineering team, and specifies the critical precautions necessary to mitigate risks.

The verdict of this analysis is that the Phase 2 plan is strategically sound and directionally correct. Its success is critically dependent on three factors: the masterful management of system complexity, the cultivation of deep cultural adoption, and the justification of a significant, multi-year investment. The projected returns, including a 30-50% reduction in design time and up to 40% improvement in power efficiency, are aggressive but directionally plausible when compared to industry case studies.

To ensure the successful execution of this pivotal phase, this report puts forth the following key recommendations for executive leadership:

• Execute the Phased Rollout with Discipline: Adhere to the detailed timeline in the Phase 2 Execution Plan, ensuring that foundational elements like the PPA and Verification agents are deployed and stabilized before broader expansion.
• Prioritize Governance and MLOps: The MLOps framework, including CI/CD for agents and observability via LangSmith, must be treated as a prerequisite for scaling, not a subsequent support function. This is essential to manage complexity and enable the "citizen developer" culture envisioned.
• Drive Adoption Through a Comprehensive Culture Program: Aggressively implement the expanded People, Culture, and Adoption plan. This includes providing tailored, role-specific training, fostering a community of innovation through hackathons and "show-and-tell" sessions, and creating formal incentive programs to drive engagement.
• Adopt a Phased, Value-Driven Funding Model: While securing commitment for the long-term vision, Phase 2 funding should be gated by the achievement of concrete, measurable KPIs tied directly to the PPA and productivity benchmarks outlined in this report. This ensures accountability and demonstrates tangible value creation at each stage.

This section translates the high-level strategy of the "Walk" phase into a concrete, actionable plan. It is designed to be used by engineering managers to structure team activities, allocate resources, and navigate the transition to an AI-driven design methodology from Q1 2027 to Q4 2028.

Execution is structured into two parallel workstreams: Advanced Agent Deployment and People & Culture Integration.

This workstream focuses on developing and integrating the cornerstone agents for Phase 2, while continuing to advance the agents developed in Phase 1.

• Q1-Q2 2027 (Architecture Agent Enhancement): Evolve the Spec Analyst and Design Space Exploration (DSE) agents. The Spec Analyst will be enhanced to not just identify ambiguities in requirements but to proactively suggest formal specifications based on historical PPA data from similar past projects stored in the MCP Server. The DSE Agent will be upgraded to use more sophisticated, AI-driven PPA estimation models, creating a direct feedback loop from the results of the now-deployed PPA Optimization Agent to improve the accuracy of its architectural trade-off analysis.
• Q3-Q4 2027 (RTL Agent Enhancement): Upgrade the Verilog/VHDL Coder Agent by fine-tuning its underlying LLMs on the company's growing internal corpus of verified RTL code from the MCP Server. This addresses the known scarcity of public HDL data and improves the quality and reliability of generated code.
• Q1-Q2 2028 (TDD Loop & HLS Integration): Enhance the Test-Driven Development (TDD) loop. The Testbench Generator Agent will be upgraded to not only create functional tests but also to generate initial SystemVerilog Assertions (SVA) for the AutoDV formal verification agent, creating a stronger link between RTL generation and formal verification. Concurrently, pilot a new HLS Coder Agent that uses the same TDD workflow to generate optimized Verilog/VHDL from C++/SystemC specifications, raising the level of design abstraction for suitable modules.

• Q1 2027: Procure and deploy commercial RL-based EDA tools (e.g., Synopsys DSO.ai, Cadence Cerebrus) to establish a performance baseline.
• Q1-Q2 2027: Form a dedicated PPA Agent Team, a cross-functional group of physical design engineers and ML/RL specialists.
• Q2-Q3 2027: Integrate the commercial tools with the MCP Server via its standardized API. The PPA Agent Team will begin developing custom RL models, trained on the company's proprietary historical design data, to augment the commercial tools.
• Q4 2027: Begin a pilot program, testing the PPA agent on a live, mainstream (but not business-critical) chip design project. The agent will run in "co-pilot" mode, with its suggestions reviewed and implemented by the human team.
• Q1 2028 Onwards: Fully deploy the PPA Optimization Agent as the standard, default methodology for all new mainstream chip designs.

• Q1 2027: The Core AI Platform Team will begin building the AutoReview and AutoDV agents. This includes integrating key formal verification (e.g., JasperGold) and static analysis tools into the MCP server's tool abstraction layer.
• Q2-Q4 2027: Develop the "verification-in-the-loop" logic within LangGraph, where the Coder Agent's output is automatically routed to the AutoReview and AutoDV agents for immediate feedback.
• Q1 2028: Implement advanced coverage-driven test generation capabilities, where the AutoDV agent can analyze coverage reports and autonomously write new tests to close identified gaps.
• Q2 2028: Integrate the full verification agentic workflow into the main CI/CD pipeline, making it a mandatory, automated check for all RTL code submissions.

This workstream is critical for ensuring the successful adoption and long-term value of Project Chimera. It moves beyond simple training to foster a deep-seated, AI-first engineering culture. The plan is built on four pillars: Structured Learning, Community & Innovation, Incentives & Recognition, and Leadership & Communication.

The "Agent Academy" will be a permanent, evolving educational program, not a one-time training event. It will be modeled on the successful, role-specific skilling initiatives at technology leaders like Microsoft.

• Action 1 (Q2 2027): Curriculum Development. A cross-functional team (HR, Core AI Team, Senior Design Engineers) will develop the curriculum.
• Action 2 (Q3 2027): Launch Tiered & Role-Specific Learning Paths. The Academy will offer distinct certification tracks tailored to different roles, moving beyond a one-size-fits-all approach :
  • Level 1: AI Essentials (All Engineers): A mandatory introductory course covering the basics of LLMs, responsible AI use, and practical prompt engineering for daily tasks, inspired by Google's "AI Essentials".
  • Level 2: Agent Orchestrator (Power Users/Team Leads): A hands-on track focused on advanced workflow design in LangGraph, debugging agent behavior with LangSmith, and curating agent teams for specific projects.
  • Level 3: Citizen Agent Developer (Advanced): A specialized track for engineers who want to build, test, and deploy their own custom agents. This will cover using Python for EDA automation and the formal process for registering agents with the MCP Server.
• Action 3 (Ongoing from Q3 2027): Make Learning Engaging and Continuous. To drive sustained engagement, the Academy will:
  • Gamify Learning: Introduce challenges, leaderboards, and "hackathon"-style projects within the learning paths to make skill acquisition more interactive and fun, similar to Microsoft's "SkillUp AI Challenge".
  • Provide a Sandbox: Offer a dedicated, low-pressure "AI Garage" environment where engineers can freely experiment with building agents without impacting production workflows.
  • Ensure Content is Current: Implement a plan to regularly update all training content to reflect the latest agent capabilities and best practices.

This pillar focuses on creating a vibrant internal community that encourages collaboration and bottom-up innovation.

• Action 1 (Q1 2028): Host Quarterly "Agent Hackathons". These events will challenge teams to build custom agents that solve a specific, real-world pain point in their workflow, with winning projects receiving recognition and resources for further development.
• Action 2 (Q2 2028): Establish a Formal "Citizen Developer" Program. This program, modeled on Microsoft's successful governance of its Power Platform , will:
  • Provide a clear, documented pathway for engineers to submit their custom-built agents for validation.
  • Implement a rigorous review process by the Core AI Platform team to ensure any new agent meets standards for security, reliability, and efficiency before being integrated into the central MCP Server.
  • Create a shared repository where validated, engineer-built agents are discoverable and reusable by the entire organization.
• Action 3 (Ongoing): Launch "AI Show-and-Tell" Sessions. Host regular, informal sessions where engineers can demonstrate the custom agents they've built, share successes, and discuss challenges. This fosters peer-to-peer learning and builds collective confidence in the new tools.

To actively encourage participation and reward innovation, a formal incentive structure will be established.

• Action 1 (Q2 2028): Implement a Recognition Program. Create highly visible awards such as "Agent of the Quarter" or "Automation Innovator of the Year" to celebrate individuals and teams who create high-impact agents.
• Action 2 (Q3 2028): Link AI Contribution to Performance. Integrate contributions to the Chimera ecosystem (e.g., achieving Level 3 certification, developing a widely adopted custom agent) into the formal engineering career progression and performance review framework.
• Action 3 (Ongoing): Offer Spot Bonuses. Provide tangible rewards for the development of custom agents that result in significant, measurable improvements to key metrics like PPA, verification time, or design cycle reduction.

Top-down reinforcement is essential for driving this cultural transformation.

• Action 1 (Ongoing): Consistent Executive Messaging. Leadership must consistently communicate the strategic vision, emphasizing that the goal is engineer augmentation, not replacement. This narrative is crucial for alleviating fear and building trust.
• Action 2 (Ongoing): Create Open Feedback Channels. Establish regular forums (e.g., town halls, anonymous surveys, Q&A sessions with the AI Platform team) to gather feedback, address concerns, and transparently communicate the project's progress, including both successes and setbacks.
• Action 3 (Ongoing): Lead by Example. Managers and team leads will be expected to be among the first to complete the "Agent Orchestrator" training and actively use agentic workflows in their project planning and execution, demonstrating commitment from the front line.

The introduction of AI agents will transform the roles of the company's core engineering teams, elevating their focus from manual execution to strategic supervision.

Managers must be vigilant about the following risks during Phase 2 execution.

• Managing Complexity: The greatest technical risk is creating a chaotic, unmanageable system. Mitigation: Strictly enforce the Supervisor-Worker architecture to maintain control. The MLOps team must implement a robust CI/CD pipeline for agents before they are deployed, using evaluation-driven development to prevent performance regressions. Monitor the Supervisor agent for performance bottlenecks as the number of worker agents increases.
• Ensuring Reliability (The "Black Box" Problem): The probabilistic nature of AI is at odds with the deterministic needs of chip design. Mitigation: Do not blindly trust AI outputs. Enforce the "gauntlet" approach where all AI-generated code is subjected to a rigorous, automated battery of deterministic checks, including the TDD workflow and the AIvril formal verification framework. LangSmith observability is non-negotiable for providing the transparency needed to debug failures and build trust.

• Overcoming Cultural Resistance: Engineers may fear being replaced or distrust AI systems they don't understand. Mitigation: Proactive and consistent communication from leadership and management is critical. Frame the initiative as augmentation, not replacement, emphasizing the new, more strategic roles. The "Agent Academy" and internal "show-and-tell" sessions for successful agent-led projects are the primary tools for building skills, confidence, and trust.
• Governing Citizen Development: Empowering engineers to build custom agents can lead to a "wild west" of redundant, unmaintained, and insecure tools. Mitigation: Model Microsoft's successful approach with its Power Platform. Any engineer-built agent must pass through a formal validation, security, and registration process managed by the Core AI Platform team before it can be added to the central MCP Server and used in a production workflow. This balances creative freedom with systemic stability.

The core of Project Chimera's Phase 2 is the scaled deployment of autonomous agents for Power, Performance, and Area (PPA) optimization and functional verification. A review of the current market demonstrates that this approach aligns perfectly with the industry's state-of-the-art.

These benchmarks show that Chimera's targets are ambitious but directionally aligned with industry progress. The key to achieving them lies in the successful integration of these capabilities into a single, unified system.

A technological transformation of this scale cannot succeed solely on the merits of its architecture. Its success is inextricably linked to the human element. The plan requires a deliberate strategy to evolve the role of the engineer, provide new skills and tools, and cultivate a culture of trust and continuous improvement. The "Agent Academy" and "Citizen AI Development" initiatives outlined in the execution plan are designed to address this, drawing inspiration from successful, large-scale AI skilling programs at companies like Microsoft and Google. These programs succeed by offering role-specific learning paths, balancing empowerment with strong governance, and making learning continuous and engaging.

Project Chimera Phase 2 represents a significant financial commitment, likely exceeding £30 million over its three-year duration, driven by costs for specialized talent, compute infrastructure, software licensing, and custom development. The justification for this investment lies in the transformative returns, which include a projected 30-50% reduction in design time and up to 40% improvement in power efficiency. These figures are supported by real-world ROI benchmarks. For example, improvements in PPA translate directly to financial value through lower cost of goods sold (more chips per wafer) and premium product pricing. Furthermore, reducing time-to-market by even a few weeks can be worth hundreds of millions of pounds in revenue for a high-volume product. The most powerful long-term return is the amplification of engineering talent, where a single engineer can achieve the work of a much larger team, enabling the company to pursue more innovation with greater agility.

A critical financial decision for Phase 2 is the implementation of an optimized hybrid compute strategy. The research highlights the massive and, critically, bursty nature of the compute workloads required for AI-driven design optimization. Relying solely on the company's existing on-premise server racks to handle these peak loads would require significant over-provisioning, leading to high capital expenditure and inefficient use of hardware that would sit idle much of the time. The most cost-effective approach is a hybrid model: leveraging our existing server racks for steady-state, baseline workloads while using the cloud for elastic, on-demand capacity to handle bursty optimization runs. Cloud providers offer the agility to dynamically scale compute resources up for an intensive, multi-week optimization run and then scale them back down, paying only for what is used. This can lead to dramatic cost savings, with the potential to reduce compute costs by up to 90% through the use of EC2 Spot Instances. The key to unlocking this hybrid model is the robust implementation of the Zero-Trust security architecture detailed in the plan, which is precisely what is needed to securely run sensitive workloads across both on-premise and public cloud infrastructure.

• Viability: The strategic direction of Project Chimera Phase 2 is strongly aligned with the technological trajectory of the entire semiconductor industry. Its core technical pillars are validated by the widespread adoption and documented success of similar approaches by leading EDA vendors and fabless design houses.
• Risk Profile: The project's primary risk is not technical but executional. The significant challenge lies in integrating proven AI components into a single, cohesive multi-agent system, navigating the profound cultural shift required, and sustaining the long-term financial investment.
• Competitive Potential: If executed successfully, Project Chimera will forge a formidable and compounding competitive advantage. The proprietary knowledge base housed within the MCP Server will create a "data moat"—a self-improving repository of encoded institutional knowledge that becomes increasingly difficult for competitors to replicate over time.

To navigate the risks and realize the competitive potential of this pivotal initiative, the following actionable recommendations are proposed:

1. Prioritize Governance and MLOps as a Foundational Prerequisite. Before scaling the deployment of more autonomous agents, the primary focus must be on building out the robust MLOps and governance framework detailed in the execution plan. This includes implementing a rigorous CI/CD pipeline for agents and ensuring comprehensive observability via LangSmith. This framework is the essential scaffolding required to manage complexity and mitigate risk.
2. Structure the "Agent Academy" for Deep Cultural Impact. A generic training course is insufficient. The "Agent Academy" must create distinct, role-specific learning paths for different engineering disciplines, design a hands-on curriculum that solves real problems, and establish a program for continuous community engagement. The goal is not just to teach a new tool but to cultivate a new, AI-first mindset.
3. Mandate and Resource an Optimized Hybrid Compute Strategy. The financial and operational advantages of a flexible hybrid compute model are too significant to ignore. While our existing server racks provide a cost-effective baseline, they cannot efficiently handle the massive, bursty workloads required for AI training and optimization. Leadership should immediately commission a detailed cost-benefit analysis of a hybrid strategy that leverages our on-premise assets for steady-state tasks while using a major cloud provider for elastic, on-demand capacity. This initiative should be resourced to work in lockstep with the security team to implement the Zero-Trust architecture outlined in the Chimera plan, ensuring that sensitive IP can be protected across both on-premise and cloud environments.
4. Enforce End-to-End Metrics for All Agent Development. Drawing a direct lesson from academic research , all AI agent development must be mandated to use reward functions tied to final, signoff-quality metrics. The Supervisor agent must evaluate its workers based on final PPA and verification coverage, not misleading intermediate surrogates.
5. Fund for the Long-Term Vision, but Gate Investment on Short-Term Value. Secure executive buy-in for the full five-year vision. However, structure the funding for Phase 2 in tranches that are explicitly gated by the achievement of specific, measurable KPIs drawn directly from the execution plan and benchmarks in this report (e.g., "achieve a >15% PPA improvement on the first projects using the PPA agent" ). This maintains long-term focus while enforcing short-term accountability and demonstrating tangible value creation at each step of its journey.