Solution entry · Agents and AI
Agent Team Starter Kit
A configurable generator and template set for scaffolding specialized AI agent roles around a visible GitHub Issues work queue and outcome gates.
What changed in the design.
Customer-neutral framing keeps the pattern reusable without claiming private results.
Before the solution
Complex work can arrive as an unstructured request with unclear ownership, hidden handoffs, and no durable record of what each specialist contributed.
What was built
The public starter kit packages a Python generator, configurable persona and workflow templates, GitHub Issues and Actions patterns, and Azure Functions and Azure OpenAI integration scaffolding.
Value enabled
The pattern is designed to make multi-agent work easier to assign, inspect, and govern while keeping the work item and its history visible to people.
How the public pattern fits together.
A GitHub issue moves through workflow templates and generated persona scaffolding to a human outcome gate.
A GitHub issue holds the request, context, and visible work state.
GitHub Actions and issue-handling templates describe routing and intervention points.
Configurable specialist files scaffold bounded roles that deployers must complete and wire.
A person can inspect the work record and decide whether the outcome is acceptable.
What the project demonstrates.
- Generates configurable specialist persona and workflow files.
- Uses GitHub Issues and Actions templates for a durable, visible work queue.
- Provides scaffolding for Azure Functions and Azure OpenAI integration.
- Includes outcome framing, validation, and human intervention patterns.
- GitHub keeps the request, discussion, and resulting work visible rather than hiding them in a transient chat.
- Specialist roles and outcome gates can be configured for the workflow instead of granting one agent an undefined remit.
- People remain responsible for reviewing consequential outputs and deployment choices.
- This is scaffolding rather than turnkey autonomy: generated personas include TODOs, the runner is a stub, and deployers must complete the domain logic and wiring.
- Setup requires Python, authenticated GitHub CLI access, Azure Storage, Azure OpenAI, and secured repository permissions.
- Evaluation, monitoring, cost controls, and domain-specific safety checks still need to be designed for each use.
Follow the evidence.
These public sources support the entry. No private customer material or internal evidence was used.
- Agent Team Starter Kit public repository Public README, quick start, generator, workflow templates, and orchestrator source are the evidence base for this entry.
- GitHub public repository metadata Repository created 2026-04-15; last public push 2026-06-02. These are repository facts, not adoption evidence.