Architecture | Tool-Call Fine-Tune Lab

System Architecture - tool-call-finetune-lab

This document is the system-level architecture attachment for the repository. It keeps the technical stack, runtime boundary, data/control flow, deployment surface, and operating assumptions in one place.

Architecture Summary

Repository-local proof surface for agent runtime reliability and AI workflow orchestration, backed by Python service or lab runtime, Container build surface, Local compose environment.

Runtime And Data Flow

flowchart LR
    User["User or operator"] --> Surface["Public demo, CLI, package, or README surface"]
    Surface --> Runtime["Runtime boundary: Python service or lab runtime, Container build surface, Local compose environment, GitHub Actions validation"]
    Runtime --> Control["Control plane: configuration, policies, adapters, and jobs"]
    Control --> Data["Data and artifacts: fixtures, reports, logs, exports, or model outputs"]
    Runtime --> Observability["Observability and validation hooks"]
    Observability --> Handoff["Documented handoff and operating boundary"]
    Data --> Handoff

Primary domain: agent runtime reliability and AI workflow orchestration.

Stack Surface

Cloud Or Local Deployment Boundary

Operating model: stateless runtimes, provider adapters, queue-aware execution, telemetry, and controlled secret boundaries

Deployment patterns

• Containerized runtime path suitable for repeatable local, staging, or managed service deployment
• Stateless agent gateway with provider abstraction, retries, cost controls, and trace capture

Control boundaries

• identity boundary and least-privilege service access
• environment separation for local, staging, and managed runtime paths
• secret storage outside source and deterministic fallback for missing credentials
• observability hooks for logs, metrics, traces, and audit events
• rollback path for deployment, schema, and model changes

Resilience controls

• bounded retries with explicit failure states
• health/readiness checks before operator-facing flows are trusted
• idempotent data or artifact writes where repeat execution is possible
• cost and quota guardrails for hosted services and model adapters

AI And Automation Boundary

Operating model: tool calling, retrieval, eval gates, prompt/version control, cost accounting, and deterministic fallback paths

Engineering patterns

• Treat tool calls as typed contracts with retries, timeouts, validation, and trace identifiers
• Evaluate agent behavior with replay fixtures, golden traces, and cost/latency accounting
• Separate deterministic checks from model-generated output so the system remains testable without external credentials
• Capture prompts, inputs, outputs, and decision metadata as inspectable artifacts instead of hidden side effects
• Gate model-assisted actions with policy, confidence, and fallback states before they reach an operator path

Evaluation and model-risk controls

• deterministic fixtures for CI-safe verification
• golden output or schema checks for generated artifacts
• trace capture for prompts, tool calls, inputs, and outputs
• quality gates that fail closed when evidence is missing

Risks to keep explicit

• prompt drift
• tool-call ambiguity
• provider outage
• unbounded latency or spend

Attached Architecture References

• Service architecture
• Cloud + AI architecture
• Architecture manifest
• Product operating model
• Quality gate

Local Architecture Guard

python3 scripts/validate_architecture_blueprint.py

CI workflow: .github/workflows/architecture-blueprint.yml.

Update this document whenever runtime entrypoints, data stores, hosted services, model/provider boundaries, or operating assumptions change.