<?xml version="1.0" encoding="UTF-8"?>
<reference anchor="I-D.mackay-aacp" target="https://datatracker.ietf.org/doc/html/draft-mackay-aacp-03">
   <front>
      <title>Agent Action Compression Protocol (AACP) Version 1.4</title>
      <author initials="A." surname="Mackay" fullname="Andrew Mackay">
         <organization>Independent</organization>
      </author>
      <date month="June" day="17" year="2026" />
      <abstract>
	 <t>   This document defines the Agent Action Compression Protocol (AACP), a
   typed coordination format for agent-to-agent communication in multi-
   agent large language model (LLM) systems.  AACP transforms natural
   language coordination instructions into deterministic, machine-
   parseable packets that can be validated before transmission, logged
   as structured audit records, and replayed consistently across
   workflow runs.

   AACP addresses a coordination content layer that existing agent
   protocols do not cover.  The Model Context Protocol (MCP) and Agent-
   to-Agent Protocol (A2A) operate at the tool access and routing layers
   respectively.  Neither specifies what agents say to each other inside
   coordination messages.  AACP fills this gap with a shared, typed
   vocabulary for agent coordination intent.

   For known workflow types, a rule-based encoder produces AACP packets
   deterministically at zero LLM cost.  A four-tier fallback extends
   this to novel instructions: community registry lookup at zero cost;
   local cache lookup at zero cost; pattern matching at zero cost; LLM
   encoding for genuinely novel instructions, logged to registry for
   permanent reuse.  An amortisation benchmark across 240 encoding
   operations demonstrated 91.6 percent cost saving versus per-call LLM
   encoding, with 6 LLM calls required across the full run.

   As a secondary benefit, AACP reduces coordination token usage by
   approximately 23 percent versus equivalent natural language
   instructions.  Framework integration benchmarks demonstrate 18
   percent total workflow cost reduction in LangChain (59 coordination
   hops) and 30 percent in CrewAI (59 coordination hops), with all
   coordination LLM calls eliminated in both cases.

   This document updates draft-mackay-aacp-02 with: framework
   integration results for AutoGen (55 percent total cost reduction, 59
   coordination hops) and Pydantic AI (85 percent total cost reduction,
   59 coordination hops); a four-framework comparison demonstrating that
   AACP saving scales with framework coordination verbosity; a new
   framing for typed-result frameworks showing that AACP completes the
   determinism picture by adding typed instructions to complement
   existing typed results; and publication of five packages on PyPI and
   npm covering all four frameworks.

	 </t>
      </abstract>
   </front>
   <seriesInfo name="Internet-Draft" value="draft-mackay-aacp-03" />
   
</reference>
