Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
Cisco and Meta are championing open-source large language models (LLMs) for enterprise threat defense, announcing new models and initiatives at RSAC 2025. Cisco's Foundation-sec-8B LLM and Meta's AI Defenders Suite aim to provide scalable, secure, and cost-effective cybersecurity solutions through collaboration and open innovation.
AGNTCY is building the Internet of Agents to be accessible for all, focusing on innovation, development, and maintenance of software components and services for agentic workflows and multi-agent applications.
**Discover:**
**1. Agent directory**
- Registry for agent publishing and discovery
- Tracks reputation and quality
**2. Open agent schema framework**
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
**Compose:**
**1. Agent connect protocol and SDK**
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
**What could these look like in action?**
A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
**Discover:**
**1. Agent directory**
- Registry for agent publishing and discovery
- Tracks reputation and quality
**2. Open agent schema framework**
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
**Compose:**
**1. Agent connect protocol and SDK**
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
**What could these look like in action?**
A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
AGNTCY is an open-source collective building infrastructure for AI agents to collaborate, led by Cisco, LangChain, Galileo, and other contributors. The initiative aims to create an open, interoperable foundation for agentic AI systems to work together seamlessly across different frameworks and vendors.
AGNTCY plans to develop key components such as an agent directory, an open agent schema framework, and an agent connect protocol to facilitate this interoperability.
AGNTCY plans to develop key components such as an agent directory, an open agent schema framework, and an agent connect protocol to facilitate this interoperability.
A consortium of Cisco, Galileo, and LangChain proposes an open, scalable way to connect and coordinate AI across different frameworks, vendors, and infrastructure to manage the rapid evolution of AI agents.
SIEM market is undergoing significant changes, with many vendors included in Gartner's latest Magic Quadrant report. However, amid the cloud transformation, vendor consolidation, and competition and integration with extended detection and response (XDR), the future is uncertain. The recent acquisition of Splunk by Cisco and IBM selling its QRadar software-as-a-service (SaaS) assets to Palo Alto Networks indicate that some big players are shifting strategies. Gartner Distinguished VP Analyst Peter Firstbrook suggests that IBM is giving up on SIEM and SOAR markets in favor of XDR, while Cisco is trying to figure out how to integrate Splunk with its XDR strategy. Many existing SIEM vendors will either be acquired, shift toward niche markets, or go out of business. Over the next 10 years, the SIEM market will transition to a few dominant players offering integrated platforms supplemented by specialized partner solutions.
