Self-Directive

The self-directive is the AI agent’s persistent memory within a workspace. It stores schema quirks, user preferences, learned rules, and anything the agent discovers that should be remembered across conversations.

How It Works

Each workspace has a self-directive — a text document (up to 10,000 characters) that the agent can read and update. It persists across all chat sessions in that workspace.

When the agent starts a conversation, it can read the self-directive to recall what it learned before. When it discovers something new — like a column naming convention, a data type gotcha, or a user preference — it writes it to the self-directive.

Tools

Tool	Operation	Purpose
read_self_directive	—	Read the current self-directive
update_self_directive	set	Overwrite the entire directive
	append	Add content to the end
	prepend	Add content to the beginning
	find_and_replace	Replace a specific section
	insert_after	Insert content after a marker
	delete_section	Remove a section

What Gets Stored

Typical self-directive content:

• Schema notes: “The users table stores created_at as Unix timestamp (seconds), not ISO date”
• Naming conventions: “This workspace uses snake_case for all table names”
• Query patterns: “Always filter by workspace_id when querying the events table”
• User preferences: “User prefers CTEs over subqueries”
• Data quirks: “The amount column in charges is in cents, divide by 100 for display”

The Agent’s Memory Loop

1. Agent encounters a new schema → inspects it → discovers a quirk
2. Agent checks self-directive for existing notes about this schema
3. Agent saves the quirk using update_self_directive
4. Next conversation, agent reads self-directive → already knows the quirk → skips re-discovery

This means the agent gets faster and more accurate over time for recurring work in the same workspace.