Model-Specific Agents For Git-Core Protocol: A Deep Dive

Overview

In the realm of Git-Core Protocol, the implementation of model-specific custom agents is a significant stride towards optimized AI assistance. These specialized agents, defined by .agent.md files, are meticulously crafted to harness the unique strengths and tool-calling patterns inherent in each supported Large Language Model (LLM) within VS Code Copilot. This approach ensures that users receive tailored support that aligns seamlessly with the capabilities of the model they are employing. In essence, each agent is fine-tuned to act as a dedicated expert, leveraging the specific nuances of its target model to provide the most effective assistance. This involves a deep understanding of each model's architecture, strengths, and limitations, allowing the agent to make informed decisions about tool utilization and task execution. The overarching goal is to create a symbiotic relationship between the user, the AI, and the underlying model, resulting in a more fluid and productive workflow. The development of these agents is not merely about creating individual entities; it's about building a cohesive ecosystem where each component works in harmony to deliver an unparalleled user experience. This involves careful consideration of how agents interact with each other, how they handle complex tasks, and how they adapt to evolving user needs. The creation of model-specific custom agents is a dynamic process, requiring continuous monitoring, evaluation, and refinement to ensure optimal performance and alignment with user expectations.

Goal

The primary goal of implementing model-specific custom agents is to enable Git-Core Protocol users to automatically receive optimized AI assistance that corresponds to the model they are utilizing. This involves a multi-faceted approach, encompassing the customization of system prompts and instructions, tool configurations, handoffs between specialized agents, and the adoption of best practices tailored to each provider. This endeavor seeks to create a seamless and intuitive experience where the AI assistance feels like a natural extension of the user's workflow. By tailoring system prompts and instructions to each model, we ensure that the AI understands the context and requirements of the task at hand. This involves crafting prompts that are clear, concise, and aligned with the model's specific capabilities. Furthermore, the configuration of tools is crucial in enabling the agents to effectively interact with the environment and perform the necessary actions. This includes selecting the appropriate tools for each task, configuring their parameters, and managing their interactions. The handoff between specialized agents is another key aspect of this goal, allowing for the seamless transition of tasks between agents with different expertise. This ensures that complex tasks are handled efficiently and effectively, with each agent contributing its unique strengths to the overall process. Finally, the adoption of best practices for each provider ensures that the agents are aligned with the latest advancements and recommendations in the field, allowing them to deliver the highest quality assistance. The ultimate aim is to empower users with an AI-driven experience that is both powerful and intuitive, enabling them to accomplish their tasks with greater efficiency and effectiveness.

Target Models

To achieve the goal of optimized AI assistance, several target models have been identified, each with its unique strengths and provider. These models form the foundation upon which the custom agents are built, and their specific characteristics dictate the design and functionality of each agent. The following table outlines the target models, their providers, key strengths, and the corresponding agent names:

Each of these models brings a unique set of capabilities to the table. Claude Sonnet 4.5, known for its complex reasoning abilities, is well-suited for tasks that require in-depth analysis and problem-solving. Claude Opus 4.5 excels in deep analysis, making it an ideal choice for tasks that demand a thorough understanding of complex systems. Claude Haiku 4.5, with its focus on fast responses, is perfect for scenarios where speed is of the essence, such as quick issue triage. Gemini 3 Pro, with its multi-modal capabilities and vast context window, is capable of handling a wide range of tasks, including those involving images, audio, and video. GPT-5.1 Codex is specifically designed for agentic coding, making it an invaluable tool for code implementation and development. Finally, Grok Code Fast 1, with its massive context window and fast tools, is well-equipped for analyzing large codebases and handling complex projects. By leveraging the strengths of each model, the custom agents can provide users with the most effective assistance for their specific needs.

Research Findings

Our research findings have highlighted the distinct capabilities of each target model, particularly in the realm of tool calling. A comprehensive comparison of these models reveals their strengths and weaknesses, allowing us to tailor the custom agents to best utilize their potential. The following table summarizes the key findings from our tool calling comparison:

The context window is a critical factor in determining the amount of information the model can process at once. Grok 4.1 Fast boasts the largest context window at 2M, making it ideal for tasks that require analyzing large amounts of code or documentation. Gemini 3 Pro also offers a substantial context window of 1M+, while Claude 4.5 has a context window of 200K, with a 1M beta version available. GPT-5.1 Codex's context window is undisclosed, but it is known for its proficiency in agentic coding tasks.

The tool format varies between models, with Claude 4.5 using input_schema and Gemini 3 Pro using parameters. GPT-5.1 Codex and Grok 4.1 Fast both employ the OpenAI style for tool calling, which is a widely adopted standard. MCP (Multi-Call Parallelism) support allows the models to call multiple tools simultaneously, which can significantly speed up complex tasks. Claude 4.5, Gemini 3 Pro, and GPT-5.1 Codex all offer native MCP support, while Grok 4.1 Fast supports MCP remotely. All four models support parallel tools, enabling them to execute multiple tool calls in parallel.

Recommended Usage

Based on our research, we have identified the best model for various scenarios, ensuring that users can leverage the strengths of each model for optimal performance. The following table outlines our recommendations:

For complex multi-step planning, Claude Opus 4.5 is the top choice due to its superior reasoning capabilities. Its ability to break down complex problems into smaller, manageable steps makes it ideal for tasks that require strategic thinking. Claude Haiku 4.5 is recommended for quick issue triage, as its fast response times and cost-effectiveness make it well-suited for quickly assessing and prioritizing issues. When it comes to large codebase analysis, Grok 4.1 Fast's massive 2M context window allows it to process vast amounts of code, making it an invaluable tool for understanding and navigating complex codebases. GPT-5.1 Codex is specifically designed for code implementation, making it the go-to model for tasks that involve writing and debugging code. Its agentic coding capabilities enable it to autonomously generate and refine code, significantly accelerating the development process. Finally, Gemini 3 Pro is the best choice for multi-modal tasks, as its ability to process images, audio, and video makes it uniquely suited for tasks that involve multiple types of data. By following these recommendations, users can ensure that they are utilizing the most appropriate model for each scenario, maximizing their efficiency and effectiveness.

Files to Create

To implement the model-specific custom agents, we need to create several files, including custom agent files and instruction files. These files will define the behavior and capabilities of each agent, ensuring that they are aligned with the strengths of their target models.

Custom Agents (.github/agents/)

The following custom agent files need to be created:

• [ ] protocol-claude.agent.md - Claude-optimized protocol agent
• [ ] protocol-gemini.agent.md - Gemini-optimized protocol agent
• [ ] protocol-codex.agent.md - GPT Codex-optimized agent
• [ ] protocol-grok.agent.md - Grok-optimized agent
• [ ] architect.agent.md - Architecture planning (Claude Opus)
• [ ] quick.agent.md - Fast responses (Claude Haiku)

Each of these files will contain the specific configurations and instructions for the corresponding agent. The protocol-claude.agent.md file will define the behavior of the Claude-optimized protocol agent, leveraging its complex reasoning capabilities. The protocol-gemini.agent.md file will configure the Gemini-optimized protocol agent, taking advantage of its multi-modal capabilities and vast context window. The protocol-codex.agent.md file will define the GPT Codex-optimized agent, utilizing its agentic coding skills. The protocol-grok.agent.md file will configure the Grok-optimized agent, leveraging its massive context window for large codebase analysis. The architect.agent.md file will define the agent for architecture planning, utilizing the deep analysis capabilities of Claude Opus. Finally, the quick.agent.md file will configure the agent for fast responses, leveraging the speed of Claude Haiku.

Instruction Files (.github/instructions/)

In addition to the custom agent files, we also need to create instruction files that provide model-specific guidance on tool calling patterns. These files will ensure that the agents are utilizing the tools in the most effective manner.

• [ ] claude-tools.instructions.md - Claude tool calling patterns
• [ ] gemini-tools.instructions.md - Gemini tool calling patterns
• [ ] codex-tools.instructions.md - GPT Codex tool calling patterns
• [ ] grok-tools.instructions.md - Grok tool calling patterns

The claude-tools.instructions.md file will provide guidance on how to effectively use tools with Claude models, taking into account their specific input schema. The gemini-tools.instructions.md file will outline the best practices for tool calling with Gemini models, utilizing their parameters format. The codex-tools.instructions.md file will provide instructions on tool calling with GPT Codex, leveraging its OpenAI-style tool calling capabilities. Finally, the grok-tools.instructions.md file will guide the use of tools with Grok models, ensuring that they are utilized effectively within its 2M context window.

Agent Handoffs

To ensure a seamless workflow, the agents will be designed to hand off tasks to each other based on the complexity of the request and the specific requirements of the task. This handoff mechanism will allow the system to leverage the strengths of each agent, ensuring that the most appropriate agent is handling each task. The following diagram illustrates the agent handoff process:

graph TD
 A[User Request] --> B{Complexity?}
 B -->|Quick| C[claude-quick]
 B -->|Standard| D[protocol-claude]
 B -->|Complex| E[architect]

 D --> F{Implementation?}
 F -->|Yes| G[codex-protocol]
 F -->|No| H[Done]

 E --> I{Large codebase?}
 I -->|Yes| J[grok-protocol]
 I -->|No| G

The process begins with a user request, which is then evaluated for complexity. If the request is deemed quick, it is handed off to the claude-quick agent, which is optimized for fast responses. For standard requests, the protocol-claude agent is utilized, leveraging its complex reasoning capabilities. Complex requests are handed off to the architect agent, which is designed for architecture planning and deep analysis. The protocol-claude agent may then hand off tasks to the codex-protocol agent if code implementation is required. The architect agent may hand off tasks to the grok-protocol agent if the task involves a large codebase. This handoff mechanism ensures that each task is handled by the agent best suited for the job, maximizing efficiency and effectiveness.

Acceptance Criteria

To ensure the successful implementation of model-specific custom agents, the following acceptance criteria must be met:

• [ ] Each agent has model-specific system prompt
• [ ] Tool configurations match model capabilities
• [ ] Handoffs enable workflow transitions
• [ ] Instructions files provide model-specific guidance
• [ ] Documentation updated in AGENTS.md
• [ ] Version bump to 1.4.0

Each agent must have a model-specific system prompt that aligns with the capabilities and strengths of its target model. This ensures that the agent understands the context and requirements of the tasks it is handling. Tool configurations must match the capabilities of the models, ensuring that the agents can effectively utilize the tools available to them. Handoffs between agents must enable seamless workflow transitions, allowing tasks to be passed between agents based on complexity and requirements. Instruction files must provide model-specific guidance on tool calling patterns, ensuring that the agents are utilizing the tools in the most effective manner. The documentation in AGENTS.md must be updated to reflect the changes and additions made during the implementation process. Finally, the version must be bumped to 1.4.0 to reflect the new features and improvements.

References

For further information and resources, please refer to the following references:

• VS Code Custom Agents
• VS Code Prompt Files
• Anthropic Tool Use
• OpenAI Function Calling
• Gemini Function Calling

These resources provide valuable insights into the implementation of custom agents and tool calling patterns, allowing developers to create more effective and efficient AI-driven workflows. For more in-depth information on prompt engineering techniques, you can also explore resources like the one available at Prompt Engineering Guide. This guide offers comprehensive information on crafting effective prompts for various AI models, which can be highly beneficial when designing model-specific agents.