[FEAT]: Custom Agent Configuration with Context Pre-loading Support

### Describe the feature or problem you'd like to solve

Custom Agent Configuration with Context Pre-loading Support

### Proposed solution

## Summary

Enable GitHub Copilot CLI to support custom agent configurations with context pre-loading capabilities, similar to [Amazon Q Developer CLI's agent](https://github.com/aws/amazon-q-developer-cli/blob/main/docs/agent-format.md) system. This would allow developers to create specialized, context-aware agents for different projects, workflows, and domains.

## Problem Description

Currently, GitHub Copilot CLI operates as a stateless tool that provides command suggestions and explanations without persistent context or domain specialization. While the tool is excellent for general command-line assistance, developers working on complex projects or specialized domains face several limitations:

### Current Limitations

1. **No Persistent Context**: Each interaction starts fresh without awareness of project context, previous conversations, or domain-specific requirements
2. **One-Size-Fits-All Approach**: No ability to specialize the assistant for different workflows (e.g., AWS operations, Kubernetes management, data science, etc.)
3. **Manual Context Provision**: Users must repeatedly provide the same contextual information (project structure, coding standards, environment setup) in each session
4. **Limited Workflow Integration**: Cannot leverage project-specific documentation, configuration files, or organizational standards automatically

### Real-World Impact

- **AWS DevOps Engineer**: Needs agent aware of company's AWS account structure, IAM policies, and infrastructure-as-code patterns
- **Kubernetes Administrator**: Requires agent familiar with cluster configuration, custom resources, and operational procedures
- **Data Scientist**: Wants agent that understands project's data pipeline, ML frameworks, and analysis patterns
- **Enterprise Developer**: Needs agent aware of corporate coding standards, security policies, and approved toolchains

## Proposed Solution

Introduce a configuration system that allows users to define custom agents with pre-loaded context, similar to Amazon Q Developer CLI but adapted for GitHub Copilot CLI's command-focused interface.

### Core Components

#### 1. Agent Configuration Files

```json
{
"name": "aws-devops",
"description": "AWS infrastructure and DevOps operations assistant",
"prompt": "You are an expert AWS DevOps engineer familiar with our company's cloud infrastructure patterns and security requirements.",
"resources": [
"file://README.md",
"file://docs/aws-architecture.md",
"file://.github/workflows/*.yml",
"file://terraform/**/*.tf"
],
"contextPatterns": [
"**/*.tf",
"**/*.yml",
"**/Dockerfile*",
".aws/config"
],
"settings": {
"maxContextFiles": 50,
"preferredShell": "bash",
"environmentHints": ["AWS_PROFILE", "TERRAFORM_WORKSPACE"]
}
}
```

#### 2. Context Pre-loading System

- **Resource Loading**: Automatically load specified files and patterns into agent context
- **Smart Indexing**: Index common project patterns (README, config files, documentation)
- **Environment Awareness**: Detect and include relevant environment variables and tool configurations
- **Update Detection**: Refresh context when watched files change

#### 3. Agent Selection Interface

```bash
# Use default agent
gh copilot suggest "deploy application to staging"

# Use specific agent
gh copilot suggest --agent aws-devops "deploy application to staging"

# Set default agent for current directory
gh copilot config set-default-agent aws-devops

# List available agents
gh copilot agent list

# Create new agent interactively
gh copilot agent create
```

### Configuration Locations

```
# Global agents
~/.config/gh-copilot/agents/
├── aws-devops.json
├── kubernetes-admin.json
└── data-science.json

# Project-specific agents
./.gh-copilot/
├── agents/
│ └── project-specific.json
└── config.json
```

### Example Use Cases

#### AWS Infrastructure Agent
```json
{
"name": "aws-infrastructure",
"prompt": "Expert in AWS infrastructure automation using Terraform and CloudFormation",
"resources": [
"file://infrastructure/README.md",
"file://terraform/**/*.tf",
"file://.aws/config"
],
"contextPatterns": ["**/*.tf", "**/*.yml", "**/buildspec.yml"],
"environmentHints": ["AWS_PROFILE", "AWS_REGION", "TERRAFORM_WORKSPACE"]
}
```

#### Kubernetes Operations Agent
```json
{
"name": "k8s-ops",
"prompt": "Kubernetes operations specialist familiar with our cluster configuration and deployment patterns",
"resources": [
"file://k8s/**/*.yaml",
"file://helm/**/*",
"file://docs/runbooks/*.md"
],
"contextPatterns": ["**/*.yaml", "**/*.yml", "**/Dockerfile*"],
"environmentHints": ["KUBECONFIG", "KUBECTL_CONTEXT"]
}
```

## Benefits

### For Individual Developers
- **Faster Onboarding**: New team members get context-aware assistance immediately
- **Reduced Cognitive Load**: No need to repeatedly explain project context
- **Domain Expertise**: Specialized agents provide more accurate, relevant suggestions
- **Workflow Efficiency**: Context-aware suggestions reduce back-and-forth clarification

### for Teams & Organizations
- **Consistent Practices**: Agents encode organizational standards and best practices
- **Knowledge Sharing**: Senior engineer expertise embedded in agent configurations
- **Onboarding Acceleration**: New developers get immediate access to institutional knowledge
- **Compliance**: Agents aware of security policies and compliance requirements

### Competitive Advantages
- **Differentiates from Amazon Q**: GitHub-native integration with repositories, workflows, and ecosystem
- **Builds on Existing Success**: Extends proven command suggestion/explanation paradigm
- **IDE Agnostic**: Works across all development environments, not just VS Code
- **Enterprise Ready**: Supports organizational configuration and knowledge management

## Implementation Considerations

### Phase 1: Basic Agent Support
- Agent configuration file format
- Simple resource loading (local files only)
- Agent selection via CLI flags
- Basic context injection

### Phase 2: Advanced Context Management
- Smart file pattern matching
- Environment variable integration
- Context refresh and caching
- Project-specific agent discovery

### Phase 3: Enterprise Features
- Organization-wide agent sharing
- GitHub repository integration
- Team collaboration features
- Advanced security controls

## Relationship to Existing Work

This feature request builds upon and complements:

- **Issue #144**: "Agent mode similar to Copilot in VSCode" - This proposal extends that concept with persistent, configurable agents
- **Upcoming GitHub Copilot CLI**: The new agentic assistant (replacing current tool in Oct 2025) could incorporate this configuration system from the start

## Technical Feasibility

### Existing Patterns
- Amazon Q Developer CLI already demonstrates this approach successfully
- GitHub Copilot already has context injection capabilities in IDE extensions
- GitHub CLI has established patterns for configuration and extensions

### Integration Points
- Leverage existing GitHub CLI configuration system
- Reuse GitHub Copilot's context processing pipelines
- Build on established file watching and indexing patterns

## Success Metrics

- **Adoption**: Number of custom agents created by users
- **Engagement**: Increased session length and command success rates with context-aware agents
- **Satisfaction**: User feedback on relevance and accuracy improvements
- **Enterprise Uptake**: Organizational adoption and agent sharing patterns

---

**Note**: This feature would position GitHub Copilot CLI as the most advanced context-aware command-line AI assistant, combining GitHub's ecosystem integration with the flexibility demonstrated by Amazon Q Developer CLI's agent system.

### Example prompts or workflows

_No response_

### Additional context

_No response_