agent-delivery-lab

Practical patterns for delivering reliable AI and agent systems.

This repository is the public home for my work on turning promising AI workflows into systems that are easier to ship, evaluate, operate, and trust.

This first version includes a small but real delivery-readiness evaluator for AI and agent systems:

• A reusable Python module for scoring operational readiness
• A runnable example for a research workflow service
• Unit tests that validate the scoring behavior
• A structure that can grow into a broader delivery and reliability toolkit

• Agent workflow delivery
• Reliability and failure handling
• Evaluation and engineering feedback loops
• Real-time and backend execution patterns
• Practical deployment-minded AI engineering

• Delivery patterns for agent-based products
• Reliability improvements for workflow execution
• Observability and debugging approaches
• Lightweight evaluation frameworks for real usage
• Notes and prototypes around production-oriented AI systems

AI demos are easy to make look impressive. Reliable delivery is harder.

I use this repository to document and build practical approaches for closing that gap, especially for agent systems, LLM applications, and backend-heavy AI products.

• Deep research workflow engineering
• Agent execution discipline
• Backend reliability for AI products
• Delivery-oriented system design

• src/agent_delivery_lab/delivery_readiness.py: readiness scoring model and report generation
• examples/run_readiness_demo.py: runnable example for a delivery review
• tests/test_delivery_readiness.py: unit tests for the first evaluator
• pyproject.toml: package metadata for the lab

cd agent-delivery-lab
PYTHONPATH=src python examples/run_readiness_demo.py
PYTHONPATH=src python -m unittest discover -s tests

The current evaluator checks whether an AI or agent service has the controls that usually separate a demo from a dependable delivery candidate:

• retries
• timeouts
• idempotent behavior
• structured logging
• tracing
• fallback paths
• human handoff
• evaluation coverage
• load testing
• alerting
• rollback readiness

It produces:

• a readiness score out of 100
• a delivery maturity level
• a list of current strengths
• a list of concrete risks
• the most important next actions

It is small enough to understand quickly, but real enough to demonstrate engineering judgment around reliability, release readiness, and operational delivery for AI systems.

• deer-flow: deep research workflow engineering with a focus on reliability, execution, and AI delivery
• zssggle-rgb: GitHub profile repository and engineering overview