Summary
Implement a Hierarchical Fallback Strategy for the SDD multi-agent workflow. This ensures that if a primary AI model fails (due to quota, rate limits, or service downtime), the system automatically retries the task using a pre-configured alternative model, preventing pipeline stagnation and context loss.
The Problem
Currently, agent-teams-lite allows assigning specific models to each SDD phase (e.g., sdd-design -> claude-3-5-sonnet). This creates a Single Point of Failure (SPOF). If the primary provider (Anthropic, OpenAI, Google) experience:
- Rate Limiting (429)
- Quota Exceeded (402)
- Service Outage (503)
- Context Length Overflow (400)
The entire development pipeline stops. In a complex SDD flow, losing context or stopping mid-way is a major productivity bottleneck.
Proposed Solution: Agent Fallback Chains
Instead of a 1-to-1 mapping, we propose a Hierarchical Chain of Agents. Each SDD phase will have an ordered list of models (Plan A, Plan B, Plan C).
Example Configuration (config.yaml):
agents:
sdd-design:
pipeline:
- model: "anthropic/claude-3-5-sonnet" # Plan A (Premium)
temperature: 0.2
- model: "openai/gpt-4o" # Plan B (Robust Fallback)
temperature: 0.3
- model: "google/gemini-1.5-pro" # Plan C (Deep Context Fallback)
retry_policy:
max_attempts: 3
backoff: "exponential"
Technical Enhancements & Optimizations
To make this more than a simple "try/catch", I suggest the following:
- Smart Error Detection: The fallback should only trigger on infrastructure errors (429, 500, 503, 402). If the error is a logic or syntax error, the system should report it normally.
- Context Re-alignment: The orchestrator should ensure that if we switch from
Claude to GPT-4, the system prompts are adjusted (if specific tweaks are defined per model) to maintain output quality.
- Dynamic Promotion (Circuit Breaker): If a primary model fails multiple times in a session, the system should temporarily "promote" the fallback to primary status for the remainder of the process to avoid unnecessary timeouts.
- Resilience Metadata: Each generated artifact (spec, design, tasks) should include metadata indicating which model was actually used for its creation.
Advantages
- Uninterrupted Availability (99.9% Uptime): The developer never gets stuck mid-task.
- Cost/Quota Management: Allows using expensive models as primary and cheaper ones as safety nets.
- Multi-Provider Resilience: If one provider goes down, the project stays alive using another.
- Improved UX: A self-healing system creates more trust for enterprise teams.
User Story
"As a software architect, I want the system to automatically use Gemini 1.5 Flash if my Claude quota is exhausted during the task generation phase (sdd-tasks), notifying me of the change but allowing me to continue working without interruptions."
Summary
Implement a Hierarchical Fallback Strategy for the SDD multi-agent workflow. This ensures that if a primary AI model fails (due to quota, rate limits, or service downtime), the system automatically retries the task using a pre-configured alternative model, preventing pipeline stagnation and context loss.
The Problem
Currently,
agent-teams-liteallows assigning specific models to each SDD phase (e.g.,sdd-design->claude-3-5-sonnet). This creates a Single Point of Failure (SPOF). If the primary provider (Anthropic, OpenAI, Google) experience:The entire development pipeline stops. In a complex SDD flow, losing context or stopping mid-way is a major productivity bottleneck.
Proposed Solution: Agent Fallback Chains
Instead of a 1-to-1 mapping, we propose a Hierarchical Chain of Agents. Each SDD phase will have an ordered list of models (Plan A, Plan B, Plan C).
Example Configuration (
config.yaml):Technical Enhancements & Optimizations
To make this more than a simple "try/catch", I suggest the following:
ClaudetoGPT-4, the system prompts are adjusted (if specific tweaks are defined per model) to maintain output quality.Advantages
User Story
"As a software architect, I want the system to automatically use Gemini 1.5 Flash if my Claude quota is exhausted during the task generation phase (
sdd-tasks), notifying me of the change but allowing me to continue working without interruptions."