Here is a question most tech leaders have not yet asked: Who is watching your AI agents while they work?
Coralogix, a Tel Aviv-based observability platform, just raised $200 million to answer that question. The company is betting that as enterprises deploy more autonomous AI systems, they will need specialized tools to track what those systems are actually doing — and catch problems before they become expensive mistakes.
Why Traditional Monitoring Falls Short
Most enterprises already have observability tools. Platforms from Datadog, Splunk, and New Relic track application performance, server health, and user behavior. But AI agents are a different animal.
Unlike traditional software that follows predictable code paths, AI agents make decisions on their own. They interpret instructions, choose actions, and sometimes interact with other agents or external systems. A customer service agent might escalate a complaint incorrectly. A procurement agent might approve a vendor without proper checks. Standard monitoring tools were not built to catch these kinds of failures.
Coralogix is positioning itself to fill this gap. The company claims its platform can trace agent behavior across workflows, flag anomalies in real time, and help teams understand why an agent made a particular decision. Think of it as an audit trail combined with an early warning system.
The Business Case for Agent Observability
For CIOs and CTOs in India, this funding round is less about Coralogix specifically and more about what it signals. Investors do not write $200 million checks on speculation. They are betting that AI agent observability will become a required line item in enterprise IT budgets.
The logic is straightforward. As companies move from pilot projects to production deployments, the stakes increase. An AI agent handling customer data needs to comply with privacy regulations. An agent processing financial transactions needs to follow internal controls. When something goes wrong — and something will go wrong — leadership needs to explain what happened and why.
Without proper observability, you are flying blind. You might not know an agent is misbehaving until a customer complains, a regulator asks questions, or revenue takes a hit.
What Coralogix Is Actually Building
The company has not revealed every detail of its AI-specific features, but the general direction is clear. Coralogix wants to offer monitoring that understands agent workflows — the sequence of steps an agent takes to complete a task — rather than just tracking server metrics.
This includes tracking SLAs (service level agreements, or the performance standards you promise to meet), monitoring for compliance violations, and providing visibility into multi-agent systems where several AI agents collaborate or hand off tasks to each other. The platform also aims to help teams debug agent behavior, which is notoriously difficult when you cannot simply read the code to understand what happened.
Coralogix is not alone in this space. Startups like Arize AI and established players like Datadog are also adding AI-specific monitoring features. But this funding round suggests Coralogix is making a focused bet on agents specifically, not just machine learning models in general.
The Competitive Landscape Is Heating Up
Indian enterprises evaluating observability vendors should expect more options in the coming months. The major cloud providers — AWS, Google Cloud, and Microsoft Azure — will likely add agent monitoring features to their existing platforms. Specialized startups will compete on depth and flexibility.
Price and integration complexity will matter. A platform that requires months to deploy and a dedicated team to manage may not be practical for mid-sized companies. The winners in this market will be vendors who can offer quick time-to-value and clear ROI.
Vendor lock-in is also a concern. Observability data is valuable. Once you commit to a platform, switching costs can be high. Tech leaders should ask hard questions about data portability and open standards before signing multi-year contracts.
What This Means for You
If your organization is experimenting with AI agents — or planning to — add observability to your evaluation criteria now. Do not wait until agents are in production to figure out how you will monitor them.
Start by mapping your agent workflows. Identify where agents make decisions, interact with sensitive data, or trigger actions with financial or compliance implications. Then evaluate whether your current monitoring tools can provide visibility into those workflows.
Finally, run a pilot. Coralogix and its competitors offer trials. Test them against your actual use cases before committing budget. The goal is not to adopt the trendiest tool — it is to ensure you can explain what your AI agents are doing when someone important asks.