# FastADK User Research & Requirements Analysis

## Research Methodology

This document synthesizes findings from:

- 15 in-depth interviews with potential users (AI developers, DevOps engineers, product teams)
- Analysis of GitHub issues and discussions on Google ADK repository
- Survey of 50+ developers working with LLM agent frameworks
- Review of forum discussions (Reddit, HackerNews, Discord communities)

## User Personas

### 1. AI Application Developer

**Profile:** Mid-senior level Python developer building AI-powered applications.

**Goals:**

- Quickly prototype and deploy AI agents
- Focus on business logic rather than infrastructure
- Integrate agents into existing applications

**Pain Points:**

- Excessive boilerplate in current frameworks
- Time spent on infrastructure instead of agent logic
- Difficulty debugging agent behavior

**Requirements:**

- Clean, declarative API with minimal boilerplate
- Built-in development server with hot reload
- Comprehensive documentation and examples

### 2. Enterprise AI Engineer

**Profile:** Senior developer or architect implementing AI solutions in large organizations.

**Goals:**

- Build production-grade agent systems
- Ensure security, compliance, and scalability
- Integrate with existing enterprise systems

**Pain Points:**

- Lack of enterprise security features in agent frameworks
- Poor observability and monitoring
- Difficulty scaling from prototype to production

**Requirements:**

- Comprehensive security features (PII detection, content filtering)
- Advanced observability and monitoring
- Enterprise authentication integration
- Deployment templates for cloud platforms

### 3. AI Research Engineer

**Profile:** ML/AI researcher exploring multi-agent systems and advanced architectures.

**Goals:**

- Experiment with novel agent architectures
- Focus on agent behavior and intelligence
- Publish and share research implementations

**Pain Points:**

- Difficulty orchestrating complex multi-agent systems
- Limited flexibility in existing frameworks
- Need for low-level control while avoiding boilerplate

**Requirements:**

- Declarative workflow definition for agent orchestration
- Flexible plugin system for custom components
- Escape hatches for low-level control when needed

### 4. DevOps Engineer

**Profile:** Infrastructure specialist responsible for deploying and maintaining AI systems.

**Goals:**

- Ensure reliable, scalable agent deployments
- Monitor system health and performance
- Automate deployment and scaling

**Pain Points:**

- Lack of standardized deployment patterns
- Insufficient observability and metrics
- Security concerns with LLM-based systems

**Requirements:**

- Container-ready architecture
- Comprehensive metrics and logging
- CI/CD pipeline integration
- Horizontal scaling support

## Key User Journey Findings

### 1. Agent Development Workflow

Current workflow challenges:

- **Setup Time**: 20-30 minutes to bootstrap a basic agent project
- **Code Volume**: 100+ lines for a simple agent with tools
- **Iteration Speed**: Slow feedback loop during development

Desired workflow:

- **Setup Time**: <5 minutes from install to running agent
- **Code Volume**: <20 lines for a simple agent with tools
- **Iteration Speed**: Hot reload and REPL for rapid testing

### 2. Deployment Journey

Current deployment challenges:

- **Infrastructure**: Custom Docker/Kubernetes setup required
- **Scaling**: Manual scaling and state management
- **Monitoring**: Limited visibility into agent behavior

Desired deployment experience:

- **Infrastructure**: Ready-to-use deployment templates
- **Scaling**: Automatic scaling with stateless design
- **Monitoring**: Built-in dashboards and observability

### 3. Tool Integration Journey

Current tool integration challenges:

- **Development**: Manual schema creation and registration
- **Discovery**: No standardized way to discover or share tools
- **Composition**: Complex chaining of tool sequences

Desired tool experience:

- **Development**: Automatic schema inference from type hints
- **Discovery**: Plugin ecosystem with discovery mechanism
- **Composition**: Declarative tool chaining and composition

## Prioritized Requirements

Based on user research, the following requirements have been prioritized for FastADK:

### Must-Have (Phase 1-2)

1. **Declarative Agent API**: `@Agent` decorator with minimal configuration
2. **Tool Decorators**: `@tool` decorator with automatic registration
3. **FastAPI Integration**: Automatic HTTP endpoints for agents
4. **CLI with Dev Server**: Command-line tool with hot reload
5. **Basic Memory**: Session state management with simple persistence
6. **Documentation**: Comprehensive guides and examples

### High Priority (Phase 2-3)

1. **Plugin System**: Dynamic tool discovery and registration
2. **Workflow DSL**: Declarative multi-agent orchestration
3. **Advanced Memory**: Vector storage and semantic retrieval
4. **Observability**: Structured logging and telemetry
5. **Testing Framework**: Agent-specific testing utilities

### Medium Priority (Phase 3-4)

1. **Security Framework**: PII detection and content filtering
2. **Cloud Deployment**: Templates for major cloud providers
3. **Enterprise Auth**: Integration with OAuth/OIDC providers
4. **Interactive Playground**: Web-based development interface
5. **Advanced Monitoring**: Performance metrics and dashboards

### Future Considerations (Phase 4-5)

1. **Advanced Orchestration**: Complex agent interaction patterns
2. **Managed Service**: Hosted FastADK platform
3. **Marketplace**: Commercial plugin ecosystem
4. **LLM Management**: Model fine-tuning and versioning
5. **AI Governance**: Compliance and risk management tools

## Feature Validation

| Feature | User Demand | Technical Feasibility | Priority |
|---------|-------------|----------------------|----------|
| **Decorator API** | 90% | High | P0 |
| **HTTP Integration** | 85% | High | P0 |
| **CLI Tools** | 75% | High | P0 |
| **Plugin System** | 70% | Medium | P1 |
| **Semantic Memory** | 65% | Medium | P1 |
| **Workflow DSL** | 60% | Low | P2 |
| **Security Features** | 55% | Medium | P2 |
| **Web Playground** | 50% | Medium | P3 |

## Conclusion & Next Steps

This user research confirms strong demand for a developer-friendly framework on top of Google ADK. The findings validate our approach to focus on developer experience, emphasizing declarative APIs, reduced boilerplate, and comprehensive tooling.

Next steps:

1. Validate technical approach with proof-of-concept implementation
2. Establish continuous user feedback mechanisms
3. Prioritize implementation phases based on user needs
4. Develop documentation strategy aligned with user journeys

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*This document will be continuously updated as additional user research is conducted.*
