tai/AGENTS.md

AGENTS.md

Conventions

• Use plain import and from statements in Python files.
• Use the .py extension for Python source and test files.
• tai.py is the entire product in one standalone stdlib-only file: CLI entry plus the stable import tai surface, grouped into classes (instance classes for stateful clusters, static utility classes for stateless groups). It must import nothing outside the standard library; a bare copy of the file has to import and run anywhere. Canonical patch targets are tai.<Class>.<name> (or the module attribute for module-level data).
• There are two kinds of test, in two different places, on purpose — see "Testing" below for the full split. In short: unit tests are top-level test_*.py; integration tests are tests/<topic>.py (no test_ prefix) built on tai_test.py.

Testing

Unit tests: test_tai.py, test_seal.py, at the repository top level, named test_*.py, unittest-based, import tai.py as a module directly, no subprocess, fast. python3 test_tai.py / python3 test_seal.py, or a single test via python3 -m unittest test_tai.SomeClass.test_method.

Integration tests: tests/<topic>.py (e.g. tests/rag.py), deliberately not named test_*.py and not at the top level, so the two kinds are never confused. These drive the real tai.py binary as a subprocess (one-shot or REPL) and check actual CLI behavior, using tai_test.py (the framework, at the repository root) as the one and only base to build on — never a one-off subprocess/asyncio harness per file. They must exercise a real LLM call through the real binary — never a mocked model or a database write standing in for what the agent would have done; the resulting flakiness from a free, rotating backend is accepted, not engineered away. Full conventions, that rule's full rationale, the required isolation rule (always a throwaway TAI_HOME), and why verification must check vault state rather than scrape CLI rendering, are in tests/README.md — read it before adding a new one. Run with python3 tests/<topic>.py (--filter, --interactive, --timeout all supported).

Agent Overview

• tai: The main autonomous agent.
• devops: A bot that manages the DevPlace DevOps platform.

Agent Profiles

• default: Default agent profile for general tasks.
• work: Work-focused agent profile.
• dev: Development-focused agent profile.

Bot Details

• devops: A bot that automates DevPlace DevOps operations.
  • Manages workspaces, projects, and deployments.
  • Handles user authentication and access control.
  • Provides development tools and utilities.
  • Integrates with OpenShift Dev Spaces.

Key Features

• Autonomous decision making
• Multi-profile support
• Background subagent orchestration
• Secure vault storage
• Container sandbox support
• Progress tracking
• Interactive REPL
• Command-line interface

Usage Examples

1. Start the main agent:

   ./tai.py
   

2. Use a specific profile:

   ./tai.py --profile work
   

3. Run a one-shot command:

   ./tai.py what is 2+3, use the shell
   

4. Check agent status:

   ./tai.py --version
   

5. Install the agent:

   ./tai.py --install
   

Development Guidelines

• Write clear, concise code
• Follow Python conventions
• Test thoroughly
• Use the agent for documentation
• Keep code maintainable
• Document decisions
• Version control changes
• Collaborate effectively

Research Rule

All external research conducted for this project (web searches, reading documentation to settle a design question, competitive/prior-art analysis) is archived under research/, maintained under the auspices of the Molodetz Institute — Research & Retrieval Division. The full specification — folder layout, the three required document types (Source Dossier, Source Citation Card, Conclusion Document), required metadata, and the quality bar — lives in research/README.md; research/INDEX.md is the living registry and statistics page. Read research/README.md in full before writing into this archive.

The binding rule, in short: a research task is not done until it is written into research/. Every source actually consulted gets its own document (Tier 1 if read and synthesized, Tier 2 if only seen in a result listing — never skipped, never inflated past what's actually known), every decision the research produced gets its own Conclusion document in that subject's conclusions/ folder, and research/INDEX.md gets updated in the same pass. Check research/INDEX.md for an existing subject before opening a new one.