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Cisco did not donate an AI model or a complete autonomous-agent product. On July 29, 2025, it contributed AGNTCY—an open-source project for discovering, identifying, communicating with and monitoring AI agents—to the Linux Foundation. Cisco, Dell Technologies, Google Cloud, Oracle and Red Hat became formative members, with the Linux Foundation reporting more than 65 supporting companies.
For developers, AGNTCY is best understood as shared infrastructure for multi-agent systems. It is designed to help agents built with different frameworks and operated by different organizations find one another, verify claims, exchange messages and produce useful telemetry. It does not guarantee that arbitrary agents will work together automatically.
What Cisco actually contributed
AGNTCY began as a Cisco initiative in March 2025, developed with LangChain and Galileo. The contribution moved the project from a primarily Cisco-led effort into a Linux Foundation setting intended to be vendor-neutral. Cisco remains involved as a formative member and technical participant.
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The distinction matters:
- Agent logic is the model prompt, workflow, tools and business rules a developer creates.
- Agent infrastructure handles discovery, identity, networking, policy and monitoring.
- Agent protocols define particular communication patterns, such as Google’s A2A or Anthropic’s MCP.
- AGNTCY is a broader collection of schemas, directories, messaging, identity, observability and testing components.
The project’s stated goal is to let agents discover one another, verify capabilities, communicate securely and collaborate across frameworks and organizational boundaries. Its code and documentation are available through the AGNTCY GitHub organization and official documentation.
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Why interoperability is difficult
A customer-service agent might run on one model provider and framework, while a billing agent runs elsewhere with a different representation of tools and permissions. Even if both can generate text, production cooperation requires answers to harder questions:
- How does one agent find another with the required skill?
- How can it tell whether the endpoint and capability description are genuine?
- What message format, streaming method and error semantics should they use?
- Which organization authorizes a requested action?
- How are prompts, tool calls, latency and failures traced across a chain?
Hard-coded endpoints can work for a small internal application, but become difficult to maintain as agents, vendors and security boundaries multiply. AGNTCY supplies building blocks for those shared functions rather than replacing the agents themselves.
The main AGNTCY components
OASF: machine-readable capability descriptions
The Open Agentic Schema Framework (OASF) defines records for an agent’s attributes, skills, capabilities and relationships. A common schema makes capability-based search possible across otherwise different implementations. The OASF repository provides schemas, validation tools, a schema server and development workflows under an Apache 2.0 license.
Agent Directory: finding and verifying records
The Agent Directory is a federated discovery service for publishing, exchanging and finding OASF records. Its documented architecture includes a gRPC API, Go, Python and JavaScript/TypeScript SDKs, a command-line client, local daemon mode, Docker Compose and Kubernetes/Helm deployment.
Records use content-addressed storage and can be cryptographically signed and verified. A signature helps establish provenance; it does not prove that an agent is safe, competent or entitled to perform every action it advertises.
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SLIM: secure, low-latency messaging
AGNTCY documentation describes SLIM as network-level communication for multi-agent applications, including publish/subscribe and streaming patterns and MLS encryption. Messaging is only one layer: applications still need compatible task semantics, authorization and failure handling.
Identity, observability and testing
The identity work covers agent and tool identifiers, verifiable credentials and policy-based access. The Observe SDK extends OpenTelemetry-related conventions for generative-AI systems so operators can collect telemetry from distributed agent interactions.
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What Linux Foundation governance changes
A foundation home can give the code and specifications a neutral venue, invite competing vendors into the same review process and reduce dependence on one company’s product roadmap. It may also make enterprises more comfortable evaluating the project and provide a place to coordinate with adjacent efforts.
Those are governance benefits, not guarantees. Linux Foundation hosting does not make AGNTCY a universally adopted standard, ensure stable APIs, provide a commercial service-level agreement or make every component production-ready. Adoption will depend on implementation quality, security, documentation, sustained maintainers and real compatibility among users.
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- The design with built-in RISC-V MCU supports low-power fast startup, 250ms fast capture, and simultaneous loading of AI model library, enabling facial recognition to be completed within 1 second
AGNTCY compared with A2A and MCP
| Project | Origin and focus |
|---|---|
| AGNTCY | Initially open-sourced by Cisco; broader infrastructure for schemas, discovery, identity, messaging, observability and collaboration. |
| A2A | Created by Google and contributed to the Linux Foundation; focused on agent-to-agent communication and task collaboration. |
| MCP | Created by Anthropic and later contributed to the Linux Foundation’s Agentic AI Foundation; connects AI applications with tools, data sources and services. |
| AGENTS.md | OpenAI contribution defining repository-specific instructions for coding agents, not a multi-agent transport or directory. |
Google contributed A2A in June 2025; Cisco did not donate it. The Linux Foundation formed the Agentic AI Foundation in December 2025 around projects including MCP, goose and AGENTS.md. Cisco joined that foundation as a Gold member, a separate development from AGNTCY. Axios reported in August 2026 that A2A was moving into the Agentic AI Foundation’s portfolio; that organizational change does not make A2A and AGNTCY the same project.
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A concrete example
Imagine a support agent receiving a request about an invoice. It could query a directory for an agent advertising billing expertise in OASF, verify the publisher’s signed record, check policy before delegating, send the task over an approved channel, and emit traces for the request, tool calls and response. The support agent still needs to judge the billing agent’s answer, enforce customer-data rules and handle an unavailable or misleading endpoint. AGNTCY makes the surrounding plumbing possible; it does not supply business judgment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Trying the Directory locally
The documented quickstart demonstrates a local registry, not a ready-made production control plane. On macOS with Homebrew:
brew tap agntcy/dir https://github.com/agntcy/dir
brew trust agntcy/dir
brew install dirctlOn Linux, the documentation also shows downloading the binary:
curl -L https://github.com/agntcy/dir/releases/latest/download/dirctl-linux-amd64 -o dirctl
chmod +x dirctl
sudo mv dirctl /usr/local/bin/Initialize and start the local node:
dirctl init
dirctl daemon startThe quickstart lists these local services:
- HTTP API and dashboard:
localhost:8889 - gRPC Directory API:
localhost:8888 - DHT API:
localhost:8999 - OCI registry:
localhost:5555
From there, a developer can publish an OASF record and test discovery. Production deployments require decisions about federation, credentials, key rotation, availability, monitoring and version compatibility. Docker Compose and Helm paths are documented, but release numbers and deployment requirements change and should be checked in the repository before use.
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When AGNTCY makes sense
- You operate multiple agents and need capability-based discovery.
- Agents come from different vendors, frameworks or organizational teams.
- You need signed records, identity controls and distributed tracing.
- You want open components and are prepared to operate them.
It may be unnecessary for a single agent with a few fixed tools, a simple MCP integration or an internal workflow whose service discovery and monitoring are already solved. Teams seeking a fully managed control plane, mature support guarantees or stable long-term compatibility may prefer a commercial platform or a framework-native solution.
Risks and unresolved problems
Interoperability is not the same as trust. Important failure modes include:
- False or stale claims: an advertised skill may be incomplete, changed or unavailable.
- Identity without authorization: knowing who operates an agent does not establish what it may do.
- Prompt-injection propagation: malicious instructions can travel through otherwise legitimate agents.
- Excessive tool permissions: a delegated agent may be able to access more data or systems than intended.
- Observability gaps: distributed chains are harder to debug than one service.
- Protocol and semantic mismatch: compatible transport does not create shared definitions of tasks, data or errors.
- Version drift: schemas, SDKs, containers and Helm charts can evolve at different speeds.
- Operational overhead: signing, routing, lookup and telemetry add latency and maintenance work.
Federation can reduce dependence on one registry, but organizations still need to examine where metadata is stored, who operates directories and how outages or compromised credentials are handled.
What the contribution means commercially
AGNTCY is presented as open-source infrastructure; key repositories identify Apache 2.0 licensing. There is no established paid AGNTCY signup or official price in the supplied sources. The real costs are engineering time and the surrounding infrastructure: compute, Kubernetes, registries, networking, identity management, logging, security operations, integration and possibly enterprise support.
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Bottom line
Cisco’s July 2025 contribution is an open-infrastructure bet, not a giveaway of a complete AI-agent business. AGNTCY addresses the unglamorous but essential layers—describing agents, finding them, establishing identity, carrying messages and collecting telemetry. Linux Foundation governance can broaden participation and reduce single-vendor dependence, but only adoption, compatible semantics, strong authorization and reliable operations will turn those building blocks into practical interoperability.
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