Building multi-agent artificial intelligence systems presents a fundamental communication design challenge long before engineers evaluate raw model performance. According to recent reports, the AGNTCY Collective—an open-source initiative launched in March by founding members including Cisco’s Outshift, LangChain, and Galileo—is tackling this exact infrastructure layer. The collective aims to provide developers with tooling for interoperable, large-scale, distributed agentic communication, establishing foundational standards so specialized agents built across diverse frameworks can collaborate seamlessly across the enterprise and the wider internet.
Agent Discovery and Searchable Architecture
Before disparate agents can collaborate, they must be findable without hardcoding URLs into configuration files. Giovanna Carofiglio, Distinguished Engineer and Senior Director at Outshift by Cisco, highlights that specialized agents function more like subject matter experts than general-purpose workers. To facilitate this, the collective launched a discovery service that allows systems to search for agents based on four key pillars: specific skills, publishers, tool compatibility, and reputation signals. According to the reports, Galileo plays an instrumental role in helping build out this reputation tracking. Furthermore, discovery operates across a network rather than staying trapped inside a single repository, allowing remote agents to be called as a service.
Identity, Provenance, and Communication Protocols
Knowing an agent's identity and publisher is critical for enterprise security and alignment. The collective has released an agent identity component and schema modeled after the Open Cybersecurity Schema Framework (OCSF) to ensure unique identifiers and clear data provenance. On the communication front, the ecosystem divides responsibilities across different protocol layers. Model Context Protocol (MCP) handles agent-to-tool interactions, while agent-to-agent communication utilizes protocols like ACP—developed alongside LangChain—and Google’s recently proposed A2A standard. To support these interactions efficiently, Cisco introduced SLIM, a data-centric, low-latency, secure transport layer designed for group-based communication rather than rigid point-to-point links.
Unified Observability and Telemetry Schemas
Debugging distributed multi-agent systems requires a shared message shape across different development frameworks. To achieve this, the collective is extending OpenTelemetry to unify metrics, events, logs, and traces. Backed by over 50 partners including Pydantic, Traceloop, LangChain, and Galileo, this initiative aims to reconstruct the complete agentic communication graph. An SDK supporting this shared telemetry schema was released shortly before the project recordings, giving engineering teams the visibility needed to track how data passes and which remote tools are invoked.
Why Communication Architecture Dictates Multi-Agent Success
The push toward multi-agent systems represents a structural shift in how enterprises deploy artificial intelligence, moving from isolated single-model implementations to complex, decentralized swarms. As engineering teams begin orchestrating tens or hundreds of autonomous actors, system dynamics rapidly transition from individual task execution to pack-like behaviors, magnifying any underlying flaws in the initial architectural contract. Without rigorous pre-split gates—such as formally defining discovery fields, stable agent identities, and strict message schemas—enterprises risk silent integration failures, misrouted peer calls, and untraceable security vulnerabilities under zero-trust frameworks. Furthermore, managing the economic and computational overhead of these systems requires moving beyond static evaluations toward active scoring frameworks that quantify improvement margins and optimize token budgets. For developers and enterprise architects, treating agent integration as a networking and distributed systems problem rather than a pure prompt-engineering exercise is no longer optional; it is the prerequisite for scaling reliable agentic workflows into production environments safely and efficiently.
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Source: Design discovery, identity, and messages before adding agents · Architecting Reliable Agentic AI | Cisco’s Giovanna Carofiglio on the AGNTCY Collective — Chain of Thought
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