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Data Analytics Startup Coralogix Valued Over $1 Billion After $115 Million Funding Round

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Coralogix, a data analytics and AI observability platform, nearly doubled its valuation to over $1 billion in its latest funding round, the company’s co-founder and CEO Ariel Assaraf told Reuters.

The startup raised $115 million in a round led by California-based venture growth firm NewView Capital, with participation from the Canada Pension Plan Investment Board (CPPIB) and venture firm NextEquity. This funding round follows Coralogix’s $142 million raise in 2022.

Despite a wider slowdown in venture capital for enterprise software-as-a-service (SaaS) startups, driven by elevated interest rates and geopolitical tensions, AI-driven SaaS solutions continue to attract investor interest. According to PitchBook, AI-focused SaaS financing hit a record $58 billion in Q1 2025.

Coralogix’s revenue has grown sevenfold since 2022, but the company is not yet profitable, with nearly 75% of its 2024 revenue reinvested into research and development. Assaraf noted this investment-heavy approach is common among peers such as Datadog and Splunk, who also prioritized R&D before profitability.

The startup has expanded its AI capabilities, notably through the acquisition of Aporia in December 2024, which bolstered its AI observability offerings. Coralogix is aggressively growing its AI talent pool and remains open to strategic acquisitions to enhance its expertise.

In line with its AI focus, Coralogix introduced a new AI agent called “olly”, designed to simplify data monitoring with a conversational interface. Industry experts have recognized AI agents as a transformative application of artificial intelligence in enterprise IT management.

Hugging Face Unveils Free AI Agent Capable of Performing Digital Tasks Autonomously

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Hugging Face has launched a new open-source AI tool called the Open Computer Agent, designed to autonomously perform various browser-based tasks. Released as a free demo, the tool is now publicly accessible through the Hugging Face website. The AI agent can navigate web platforms like Google Search, Google Maps, and even ticket booking sites to complete actions on behalf of the user — all without direct human input at each step. This development builds on Hugging Face’s smolagents framework, which was introduced earlier this year to facilitate lightweight autonomous agents.

Announced by Aymeric Roucher, Project Lead for Agents at Hugging Face, the Open Computer Agent is powered by a virtualized Linux environment and includes applications like Mozilla Firefox. This setup allows the AI agent to interact with the web as a human would — clicking, typing, and navigating through browser interfaces in real time. With its open-source foundation, the project invites developers, researchers, and enthusiasts to explore and expand its capabilities.

The intelligence behind the agent comes from the Qwen2-VL-72B, a powerful vision-language model capable of interpreting images and interfaces based on visual coordinates. This means the agent can “see” what’s on screen, make decisions, and perform follow-up actions like clicking buttons or typing search queries. Hugging Face’s smolagents library adds the logic layer that enables these autonomous interactions, forming the basis of the agentic workflow.

Users trying out the demo can instruct the agent to carry out tasks like finding directions using Google Maps. Once prompted, the agent launches a browser, navigates to the correct site, inputs the required information, and completes the task — all without the user having to touch their keyboard or mouse. With the release of the Open Computer Agent, Hugging Face continues its push toward more accessible and transparent AI tools, empowering the public to experiment with emerging forms of digital automation.

ElevenLabs Launches Agent Transfer Feature for Seamless Data Sharing Between AI Agents

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ElevenLabs has unveiled a new enterprise-focused feature that enables seamless communication between artificial intelligence (AI) agents, introducing what they call the “Agent Transfer” feature. This feature is designed to allow one AI agent to pass a conversation on to another when certain conditions are met, ensuring a smooth handover of information. The key advantage of Agent Transfer is that it not only transfers the conversation to a new agent but also shares the history of the discussion, helping the new agent understand the context and continue the conversation seamlessly. This feature is particularly beneficial for businesses looking to create specialized AI agents with different areas of expertise, allowing them to collaborate effectively.

The announcement was made on X (formerly known as Twitter), where ElevenLabs introduced the feature as part of its broader Conversational AI toolkit. While Agent Transfer is currently available for enterprises, ElevenLabs has not clarified whether this feature will be offered as a standalone service or integrated into existing plans. The company has also provided developers with instructions on how to implement the feature through its support pages, making it accessible for businesses to integrate into their existing AI workflows.

As more companies incorporate AI agents into their operations, the challenge of avoiding data silos becomes increasingly important. Traditional AI systems often struggle with sharing data across different functions, leading to inefficiencies where information is trapped within one segment of the business. ElevenLabs’ approach with Agent Transfer seeks to address this issue by enabling AI agents to communicate directly with each other and share valuable data. This helps ensure that the right knowledge is accessible at the right time, enhancing the effectiveness of AI interactions.

The practical implications of Agent Transfer are significant. For example, if a customer service AI agent encounters a situation where it cannot adequately assist a user, the conversation can be transferred to a more specialized AI agent without requiring human intervention. The second agent receives the full conversation history, allowing it to pick up the discussion without the need for the user to repeat themselves. This not only improves the user experience but also boosts the overall efficiency of AI-driven customer service operations.