Question 1

What is PS-LANG?

Accepted Answer

PS-LANG is a zone-based syntax language for controlling what AI agents see in multi-agent workflows. It provides 7 privacy zones that let you control context flow between agents, reduce token usage by up to 60%, and create cleaner agent handoffs in AI pipelines.

Question 2

How does PS-LANG reduce token usage?

Accepted Answer

PS-LANG uses zone-based syntax to mark content with specific privacy levels. When agents process prompts, they only see content within zones they have access to, reducing unnecessary context and cutting token usage by up to 60% in multi-agent workflows.

Question 3

What are PS-LANG zones?

Accepted Answer

PS-LANG has 7 privacy zones: public (<@. .>), internal (<. .>), confidential (<#. #>), agent-specific (<.agent .agent>), bookmark (<.bm .bm>), log (<.log .log>), and system (). Each zone controls who can see the content inside.

Question 4

Is PS-LANG backwards compatible?

Accepted Answer

Yes, PS-LANG is designed to be backwards compatible. You can add zone syntax to existing prompts without breaking them. Agents that don't understand PS-LANG will simply ignore the zone markers and process the full text.

Question 5

How do I install PS-LANG?

Accepted Answer

Install PS-LANG using npm: 'npx ps-lang@alpha init'. This initializes PS-LANG in your project and sets up the necessary configuration for zone-based context control in your AI workflows.

Question 6

What is PostScript Journaling?

Accepted Answer

PostScript Journaling is PS-LANG's premium feature for tracking AI workflows, benchmarking improvements, and maintaining secure audit trails. It uses end-to-end encryption to store prompt history and workflow analytics.

Question 7

Does PS-LANG work with MCP?

Accepted Answer

Yes, PS-LANG integrates seamlessly with Model Context Protocol (MCP) agent chains. You can use zone syntax within MCP workflows to control context between different agents in your pipeline.

Question 8

How accurate are PS-LANG benchmarks?

Accepted Answer

PS-LANG improves benchmark accuracy by controlling exactly what context each agent sees. This eliminates context contamination between test runs and ensures consistent, reproducible benchmark results.