Agent Stack

Wasting time writing bespoke agent loops and ad-hoc architectures that fail in production because they lack structural enforcement?

What it does

• Enforces an eight-layer architectural stack including harness, tools, memory, and watchdogs.
• Implements a per-pass loop spec requiring explicit done-when conditions and stopping rules.
• Standardizes a pre-deploy double gate that verifies both task completion and verification quality.
• Provides a machine-checkable verifier script to validate agent configurations before deployment.
• Defines a three-tier memory system (session, long-term, pattern) to ensure continuity across runs.

Frameworks & tools

Python (standard library), MCP servers, and any LLM-based agent runtime that supports system prompts or harness injection.

Why this beats prompting it yourself

Prompting alone is a "suggestion" that models eventually ignore. This skill replaces vague instructions with a structural harness and code-level enforcement that the model cannot escape. It moves agent reliability from a "vibe" to a machine-verifiable verdict.

Use cases

• Building autonomous workers that must persist across multiple sessions without losing context.
• Standardizing agent development across a team to avoid re-deriving architectures for every new tool.
• Fixing "silent failure" modes where agents stop responding or loop indefinitely on a single task.
• Implementing hard safety gates between agent intent and tool execution.

Known limitations

The included verification script is restricted to local file analysis and does not perform live network or integration testing. Active learning patterns require a baseline of 100+ recorded experiences to be effective.