Roadmap: Phase 1 - Crawl

Project Chimera: An Actionable Implementation Plan for Phase 1

This document presents the detailed, actionable project plan for Phase 1 ("Crawl") of Project Chimera, a strategic initiative to re-architect the company's semiconductor design pipeline around a full-stack, AI-native approach. Spanning from Q3 2025 to Q4 2026, this initial 18-month phase is designed to achieve two primary objectives: first, to construct the foundational technical infrastructure, including the Multi-Agent Collaboration Protocol (MCP) Server and its associated MLOps framework; and second, to demonstrate the viability and reliability of agentic workflows through a high-impact pilot project focused on AI-driven Test-Driven Development (TDD) for RTL generation. This plan outlines a clear governance structure, a quarter-by-quarter execution roadmap, discipline-specific roles, and a comprehensive risk mitigation strategy. Success will be measured by the stable deployment of the core infrastructure, the achievement of a >95% functional test pass rate for AI-generated RTL in the pilot, and a measurable reduction in design time for the target IP block. By successfully executing this "Crawl" phase, the company will not only de-risk the broader five-year vision but also build the critical technical capabilities and cultural momentum required to scale towards full-stack AI autonomy in subsequent phases.

This section establishes the strategic context for Phase 1. It defines the specific, measurable objectives, outlines the foundational technology stack that will be deployed, and details the Key Performance Indicators (KPIs) that will be used to evaluate success. This framework ensures that all subsequent execution activities are aligned with a clear, unified purpose.

The core mandate of Phase 1 is to "build the core technical infrastructure and demonstrate the viability of the agentic approach on a limited, high-impact pilot project". This phase is fundamentally about de-risking the larger five-year vision by proving foundational concepts in a controlled environment. The scope is therefore tightly constrained to two key deliverables: a production-ready, v1.0 implementation of the MCP Server and its associated orchestration and observability platforms, and the successful execution of a single pilot project focused on an agentic Test-Driven Development (TDD) workflow for a non-critical IP block.

Beyond these technical deliverables, the strategic mandate for Phase 1 is to serve as a calculated exercise in organizational change management. The most significant barriers to enterprise AI adoption are often cultural, rooted in engineer distrust of "black box" systems and fear of displacement. The chosen pilot project—TDD for RTL generation—is not arbitrary; it is designed specifically to address the primary technical fear of AI "hallucination" in a domain where correctness is paramount. By wrapping a probabilistic technology (the generative AI Coder Agent) in a deterministic, verifiable framework (the human-vetted test suite), the project aims to prove not just that the technology works, but that it can be made trustworthy. Success in this phase, therefore, will be measured as much by its ability to build confidence and momentum as by its technical outputs.

The technology stack for Phase 1 is designed to be robust, scalable, and transparent, prioritizing control and observability.

MCP Server v1.0: The central nervous system of the agentic system. The initial version will include three critical components:

• Tool Abstraction Layer: An API gateway providing standardized access to a limited set of initial tools, including simulation software (e.g., VCS, Questa), formal verification tools (e.g., JasperGold), and custom Python scripts for parsing log files.
• Knowledge Hub (RAG): A PostgreSQL database with the pgvector extension will serve as the initial vector store. A primary action in this phase is a "large-scale data ingestion project" to populate this knowledge base with Process Design Kits (PDKs), standard cell libraries, technical manuals, and internal design guidelines.
• Context & State Management (CAG): A stateful component, likely using a Redis cache, to manage the short-term conversational history and state for the pilot project's agentic workflow.

LangGraph for Orchestration: The choice of LangGraph is a strategic one, enabling the implementation of a Supervisor-Worker architecture. This architecture is critical for maintaining the control, auditability, and debuggability that are non-negotiable in the capital-intensive semiconductor industry. This contrasts with more unpredictable "swarm" architectures that are unsuitable for high-stakes enterprise use. The maturity of LangGraph is validated by its use in production at companies like Uber for automating code migrations and at LinkedIn for orchestrating recruiting agents, demonstrating its suitability for reliable, complex workflows.

LangSmith for Observability: This platform is the cornerstone of building trust and enabling MLOps. LangSmith will be used from day one to provide end-to-end tracing of every agentic workflow. This directly addresses the "black box" problem by allowing engineers to visualize the AI's reasoning chain, tool calls, and outputs, transforming the AI from an opaque oracle into a debuggable system that fosters confidence.

A balanced scorecard of KPIs will be tracked across three domains: Infrastructure Stability, Pilot Project Performance, and Organizational Readiness.

Infrastructure Stability KPIs:

• MCP Server Uptime: >99.9%
• RAG Knowledge Base Ingestion: 100% of targeted Phase 1 documents (PDKs, manuals) successfully ingested, indexed, and queryable.
• LangSmith Integration: 100% of pilot project agent interactions traced and logged.

Pilot Project Performance KPIs:

• Primary Metric: >95% functional test pass rate for the AI-generated RTL on the first attempt (before human refactoring).
• Secondary Metric: A measurable reduction (target: >15%) in design and verification time for the pilot IP block compared to a human-only baseline. This will be measured by comparing total engineering hours for the pilot against a similar, recently completed IP block.

Organizational Readiness KPIs:

• Formation of a dedicated Core AI Platform Team.
• Identification and engagement of at least 10 "early adopter" engineers from the RTL and Verification teams.
• Successful completion of at least two "lunch and learn" sessions demonstrating the TDD pilot to the broader engineering organization.

The primary KPI—a >95% functional test pass rate—serves as a powerful proxy for building trust. In the TDD workflow, the tests are defined by humans before the Coder Agent begins its work. Therefore, this metric is not just a measure of the AI's ability to code; it is a measure of the AI's ability to adhere to a human-defined specification of correctness. A high pass rate demonstrates that the agent's behavior can be successfully constrained and directed, proving to skeptical engineers that the system is not an uncontrollable artist but a disciplined, verifiable engineering tool. Achieving this KPI is as much a cultural victory as it is a technical one.

This section translates the strategic framework into a concrete, time-bound execution plan. It provides a quarter-by-quarter breakdown of activities, defines critical checkpoints for governance, and specifies the tangible deliverables for each stage of the 18-month "Crawl" phase.

Q3 2025: Foundation and Team Formation

• Actions: Officially launch Project Chimera. Form the core AI Platform Team and the Project Governance Council. Procure necessary cloud infrastructure and deploy initial instances of LangSmith and a PostgreSQL server. Begin planning the data ingestion pipeline and select the specific, non-critical IP block for the TDD pilot project.
• Deliverable: Project Charter, finalized team structure, deployed development environment.

Q4 2025: Data Ingestion and MCP Server Scaffolding

• Actions: Execute the large-scale data ingestion project, populating the RAG knowledge base with all targeted design documentation, manuals, and library data. Develop and deploy the v0.5 MCP Server, focusing on the RAG API and the tool abstraction layer for simulation tools.
• Deliverable: Populated RAG vector database; functional MCP Server v0.5 with documented APIs.

Q1 2026: Pilot Agent Development (Testbench Generation)

• Actions: Develop the Testbench Generator Agent. Verification engineers (the designated early adopters) will work with the AI Platform Team to prompt and refine this agent to generate a comprehensive test suite for the pilot IP block based on its formal specification.
• Deliverable: A complete, human-approved testbench for the pilot IP block, generated by the AI agent.

Q2 2026: Pilot Agent Development (TDD Loop)

• Actions: Develop the Verilog/VHDL Coder Agent. Implement the full TDD loop within the LangGraph Supervisor-Worker architecture. The Coder Agent will iteratively generate RTL, which is automatically tested against the pre-defined testbench until all tests pass. RTL designers will supervise this process.
• Deliverable: First version of AI-generated RTL for the pilot IP block that passes >95% of functional tests.

Q3 2026: Refinement, Integration, and Measurement

• Actions: Human engineers refactor and finalize the AI-generated RTL. Integrate the final IP block into a test system. Conduct a full baseline comparison of engineering hours, bug rates, and development time against a comparable human-only project.
• Deliverable: Final, integrated IP block; a detailed performance report benchmarking the agentic workflow.

Q4 2026: Final Reporting, Cultural Roadshow, and Phase 2 Planning

• Actions: Prepare the final Phase 1 report for executive review. Conduct a "roadshow" of the results to the entire engineering organization to build momentum. Finalize the detailed project plan and resource allocation for the "Walk" phase.
• Deliverable: Final Phase 1 Executive Report; approved project plan for Phase 2.

• M1 (End Q3 2025): Project Kick-off & Team Onboarding. Go/No-Go for resource allocation.
• M2 (End Q4 2025): Data Ingestion Complete. Go/No-Go for agent development, contingent on successful knowledge base population.
• M3 (End Q1 2026): Testbench Generation Complete. Go/No-Go for RTL coding, contingent on a high-quality, human-approved test suite.
• M4 (Mid Q3 2026): Pilot KPI Measurement Complete. Primary review of the >95% pass rate and time reduction metrics.
• M5 (End Q4 2026): Phase 1 Final Review & Phase 2 Approval. Executive sign-off on project success and commitment to the "Walk" phase.

A detailed Gantt chart is essential for moving this plan from a high-level strategy to a day-to-day management tool. It provides granular visibility into the project's execution, allows for proactive identification of delays, manages critical dependencies between teams, and creates clear accountability. For example, the development of the Coder Agent is critically dependent on the Testbench Agent team delivering a stable and comprehensive set of tests; the Gantt chart makes this dependency explicit and trackable.

This section details the human and process layers of the project. It defines who makes decisions, how the teams are structured, how the AI systems themselves will be managed like production software, and how the various engineering disciplines will collaborate on the pilot project.

Effective AI transformation requires a shift from siloed AI teams to cross-functional squads and strong, centralized governance; a technology-only approach is a common pitfall that leads to failure.

Strategic AI Council (Executive Layer): This council will provide executive oversight, align the project with business objectives, approve major budget allocations, and resolve cross-functional roadblocks.

• Members: CTO (Chair), VP of Engineering, VP of Product, Chief Data Officer, and a senior Legal/Compliance representative.
• Cadence: Meets quarterly to review progress against milestones (M1-M5).

Chimera Phase 1 Project Team (Execution Layer): This is the full-time team responsible for the day-to-day execution of the Phase 1 plan.

• Structure:
  • Project Lead: Overall responsibility for timeline, budget, and reporting.
  • AI Platform Lead: Manages the AI Platform Team, responsible for building the MCP Server and the agents.
  • RTL Pilot Lead: A senior RTL or Verification manager who acts as the business owner for the pilot, ensuring it meets the needs of the design teams.
  • AI Platform Team (3-4 Engineers): The core developers building the agentic system.
  • Discipline Champions (Part-Time): Nominated "early adopter" engineers from System Architecture, RTL Design, and Verification who act as liaisons, provide domain expertise, and champion the project within their teams.

AI agents and workflows must be treated with the same rigor as production software, which is the foundation of reliability and trust.

CI/CD Pipeline for Agents: A dedicated Continuous Integration/Continuous Deployment (CI/CD) pipeline will be established for the TDD agents.

• Trigger: Any change to an agent's prompt, its underlying model, or its tools will trigger an automated workflow.
• Process: The pipeline will automatically run the updated agent against a "golden dataset" of test cases—a curated set of RTL design problems with known-good solutions.

Evaluation-Driven Development: The pipeline will use LangSmith's evaluation suite to score the agent's output on metrics like correctness and tool-use accuracy. A deployment to the "production" agent environment will be automatically blocked if the change causes a performance regression, ensuring the system only improves over time.

Observability with LangSmith:

• Debugging: LangSmith traces will be the primary tool for debugging agent failures. When the Coder Agent fails a test, engineers can inspect the trace to see the exact LLM calls, the code it generated, and the tool output that caused the failure, making the system debuggable.
• Human Feedback Loop: LangSmith will be used to create annotation queues. When an agent fails, the trace can be sent to an expert engineer's queue. The engineer reviews the trace, identifies the root cause, and provides a corrected example. This feedback creates a high-quality dataset used to continuously improve the agents.

This pilot project will catalyze the shift in roles from "tool user to agent orchestrator".

• System Architects: Will define the high-level functional specification and performance constraints for the pilot IP block. Their primary execution task is to author the initial "prompt" for the entire workflow, which serves as the mission statement for the Supervisor agent.
• Verification Engineers: Will shift from post-coding test writing to pre-coding "correctness definition." They will collaborate with the AI Platform Team to guide the Testbench Generator Agent, reviewing and approving the generated tests and assertions. This effectively creates the "exam" that the Coder Agent must pass, directly implementing the TDD workflow.
• RTL Designers: Will evolve into "agent orchestrators" and human-in-the-loop supervisors. They will provide the high-level functional description to the Supervisor agent and monitor the TDD loop in LangSmith, intervening only when the Coder Agent gets stuck or produces suboptimal code. Their focus shifts from line-by-line coding to high-level guidance and architectural validation. This human-in-the-loop model is essential for managing agentic systems in their current state of maturity.
• AI Platform Team: Will build, maintain, and improve the agents and the MCP Server. They will work directly with the discipline champions to translate domain knowledge into effective prompts, tools, and agent behaviors, and they will manage the CI/CD pipeline and the human feedback loop.

This section provides a proactive and transparent analysis of the primary risks facing Phase 1. It follows the structure outlined in the Project Chimera document, elaborating on each risk and its multi-layered mitigation strategy to create an actionable register for the project team.

Risk: Reliability. LLMs are probabilistic and can "hallucinate" or generate functionally incorrect HDL code, a catastrophic failure mode in chip design. The unpredictability of LLMs is a primary barrier to production deployment.

Mitigation: The entire pilot project is architected around this risk. The Test-Driven Development (TDD) workflow does not blindly trust the LLM's output. The Coder Agent is constrained by a pre-defined, human-vetted suite of functional tests. Its goal is not simply to "write code" but to "write code that passes these specific tests". This approach grounds the probabilistic LLM in a deterministic, verifiable framework, creating a tight feedback loop that catches errors immediately.

Risk: Interpretability. The "black box" nature of AI hinders trust and adoption because engineers are reluctant to use tools whose reasoning they cannot understand.

Mitigation: Radical transparency will be achieved via LangSmith. Every step of the agent's "thought process"—every LLM call, every tool use, every intermediate result—is logged and visualized. This provides maximum possible transparency, turning the "black box" into a "glass box" and allowing engineers to perform correlational analysis to build a heuristic understanding of the agent's behavior.

Risk: Coordination Complexity. As agent systems grow, their interactions can become unpredictably complex, leading to debugging challenges and performance bottlenecks. The Supervisor agent itself can become a single point of failure.

Mitigation: For Phase 1, the implementation will use the simplest, most constrained version of a multi-agent system: a strict Supervisor-Worker architecture where all communication is routed through the Supervisor. This prioritizes control and predictability over flexibility, which is the correct trade-off for an initial, high-stakes deployment. More complex architectures, like a hierarchical "supervisor of supervisors," are explicitly deferred to later phases. Performance monitoring via LangSmith will be used to proactively identify and address system bottlenecks.

Risk: Intellectual Property Theft. The MCP Server will centralize the company's "crown jewels"—proprietary IP, design data, and methodologies. A breach would be an existential threat.

Mitigation: A multi-layered, "compliance by design" security posture based on a Zero-Trust philosophy will be implemented from day one. This means no agent or user is trusted by default; every API call to the MCP Server must be individually authenticated and authorized based on the principle of least privilege. The knowledge base will be architected for strict data segregation, and every action will be immutably logged to a secure audit trail via the LangSmith tracing system, providing a complete, verifiable record for security forensics.

Risk: Cultural Resistance. Engineers may fear being replaced, distrust the AI's outputs, or resist changes to their established workflows.

Mitigation: This is a socio-technical problem requiring a multi-pronged solution.

• Narrative of Augmentation: Consistent communication from leadership will frame Project Chimera as a strategy to augment, not replace, engineers by automating tedious work.
• Building Trust Through Transparency: The use of LangSmith is the primary technical tool for building trust by allowing engineers to see how the AI works, demystifying it.
• Empowerment Through Participation: The inclusion of "Discipline Champions" in the governance model is key to building buy-in and creating internal advocates.
• Start Small, Prove Value: The "Crawl" phase approach is itself a change management strategy. By starting with a limited-scope pilot and delivering a tangible win, the project can build credibility and enthusiasm for the broader vision.

This register formalizes the risk management process, quantifies risks, and assigns clear ownership for mitigation actions. It will serve as a living document for the Governance Council to review at each milestone.

This section benchmarks the Project Chimera plan against real-world agentic AI deployments and strategic guidance from industry experts. Its purpose is to provide external validation for the chosen architecture and methodology, building confidence among stakeholders that the plan is aligned with proven best practices.

The Supervisor-Worker architecture, implemented via LangGraph, is not a theoretical or experimental choice. It is a proven, production-grade pattern for building reliable, complex agentic systems in the enterprise.

• Uber uses LangGraph to build a network of specialized agents to automate large-scale code migrations and unit test generation, a powerful parallel to the goal of automating HDL code generation. Their experience demonstrates that a hierarchical agent system can successfully tackle complex software engineering tasks with precision and control. Their general approach to large migrations involves creating abstraction layers to manage complexity, which is analogous to the MCP Server concept.
• LinkedIn built a hierarchical agent system on LangGraph for its AI-powered "Hiring Assistant," which automates candidate sourcing, matching, and messaging. This demonstrates the model's effectiveness for workflows that require a human-in-the-loop for strategic decisions.
• Anthropic's multi-agent research system uses a lead "orchestrator" agent to decompose complex research queries and delegate tasks to parallel "subagents". Their key learnings directly validate the Chimera approach: the need for detailed task descriptions for subagents, the importance of parallelization for speed, and the critical role of full production tracing for debugging.

The common thread across these pioneers is the use of a controlled, hierarchical architecture to manage complexity and ensure reliability. This stands in stark contrast to decentralized or "swarm" models, reinforcing that for mission-critical enterprise tasks, control and auditability are paramount.

The TDD pilot project is the single most important strategic choice in Phase 1, as it directly mitigates the primary risk of using generative AI in a high-stakes engineering domain: unreliability. The core problem with generative AI for code is its probabilistic nature, which can lead to functionally flawed "hallucinations". The TDD workflow inverts this process. Instead of asking the AI to "write code" and then testing it, correctness is first defined with a human-vetted test suite. The AI's task is then narrowed to "write code that makes these tests pass". This transforms the agent from an unpredictable creator into a constrained solver.

A compelling real-world analog is Redfin's development of their "Ask Redfin" chatbot. They used a TDD-like approach, systematically evaluating prompts against hundreds of test cases in a sandbox before production. This allowed them to simulate user interactions, test different scenarios, and build confidence in their assistant's behavior and performance. The agentic TDD pilot is a sophisticated application of this same core principle: closing the loop on agentic behavior with a rigorous, test-first evaluation framework, which is a best practice for creating resilient AI applications.

The structure of the Project Chimera roadmap (Crawl-Walk-Run) and the specific actions planned for Phase 1 are strongly aligned with strategic advice from leading industry analysts for successful enterprise AI transformation.

• Alignment with McKinsey Framework: The plan directly implements key actions recommended by McKinsey for operating in the agentic era: "Conclude the experimentation phase and realign AI priorities," "Redesign the AI governance and operating model," and "Launch a first lighthouse transformation project". The TDD pilot is this "lighthouse" project.
• Alignment with General Roadmaps: The plan embodies common themes from successful AI adoption roadmaps, such as starting with strategic goal mapping, beginning with a small pilot to prove value before scaling, and proactively addressing cultural change and AI literacy gaps.

The Project Chimera plan is not being executed in a vacuum. Its phased structure, focus on a foundational pilot, emphasis on governance, and awareness of cultural factors are consistent with the patterns of successful AI adoption across multiple industries. This provides strong evidence to stakeholders that the plan is not only technically sound but also strategically mature.

The successful completion of the "Crawl" phase will mark a pivotal moment in the company's transformation. It will deliver not just a set of technologies, but a portfolio of critical organizational assets:

• A Proven Technical Foundation: A stable, observable, and secure MCP Server infrastructure ready for expansion.
• A Validated Agentic Workflow: Tangible proof that AI agents can be deployed reliably and effectively to solve real-world chip design problems, evidenced by the success of the TDD pilot.
• A Nucleus of AI Expertise: A trained and experienced core AI Platform Team, complemented by a growing cohort of enthusiastic early adopters within the engineering ranks.
• Organizational Trust and Momentum: The most valuable asset of all. By demystifying agentic AI and delivering a concrete win, Phase 1 will have built the credibility and political capital necessary to accelerate into the more ambitious "Walk" phase.

Upon completion of Phase 1, the organization will be perfectly positioned to begin the "Walk" phase. The objectives for Phase 2—developing the autonomous PPA Optimization Agent and the AIvril-inspired Verification Agent, and integrating them into mainstream projects—will build directly upon the infrastructure, processes, and trust established in this foundational first stage. The "Crawl" phase is the critical first step in a long journey, but by executing it with rigor and strategic foresight, it will set the company on an irreversible path toward full-stack AI dominance.