Laminar, a developer tools startup focused on AI agent observability, has secured $3 million in seed funding led by Atlantic.vc, with participation from Y Combinator, AAL.vc, and angel investors including Ben Sigelman and Ant Wilson.
The company is developing infrastructure to help teams monitor, debug, and optimise AI agents operating in complex, long-running environments. The funding will support product development and market expansion as AI agents move toward production-scale deployment.
What The Company Does
Founded by Robert Kim and Dinmukhamed Mailibay, Laminar provides observability tools tailored to the specific challenges of AI agents. Unlike traditional software systems, AI agents often execute multi-step processes over extended periods, generating large volumes of traces and interactions that are difficult to analyse with existing tooling.
Laminar’s platform captures detailed execution data across AI workflows, including large language model calls, tool usage, and function-level operations. Its system is designed to handle long-running sessions with thousands of spans, offering developers structured visibility into how agents operate.
For browser-based agents, the platform records session activity and aligns it with trace data, allowing developers to review both the actions taken and the contextual inputs that informed decision-making. This combined view is intended to improve debugging accuracy and reduce time spent diagnosing failures.
Additional features include automated detection of anomalies and failure patterns through its Signals functionality, as well as an Agent Debugger that enables developers to restart agent workflows from specific points while preserving full execution context.
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Market Context / Industry Background
As AI agents evolve from experimental prototypes to production systems, the need for specialised observability tools is becoming more pronounced. Traditional monitoring solutions are not designed to handle the probabilistic and multi-step nature of AI-driven processes, particularly when workflows involve dynamic decision-making and external tool interactions.
The growing complexity of AI systems has led to increased demand for infrastructure that can provide transparency, traceability, and debugging capabilities. This is particularly relevant in enterprise environments, where reliability and accountability are critical.
Agent-native observability is emerging as a distinct category within the broader AI tooling ecosystem. Companies are beginning to invest in dedicated solutions or build internal capabilities to manage the operational challenges associated with deploying AI agents at scale.
Founder / Investor Commentary
Robert Kim, co-founder and CEO of Laminar, highlighted the limitations of existing tools when dealing with complex agent workflows. He explained that traditional observability systems often fail to provide actionable insights when issues occur deep within long-running processes.
“When your agent fails 40 minutes into a task, today’s tools show you a wall of thousands of spans and say ‘good luck.’ We built Laminar so you can pinpoint the exact decision that went wrong and rerun from that point,” he said.
Lukas Erbguth, Principal at Atlantic.vc, emphasised the importance of observability as a foundational layer in the AI stack. He noted that Laminar’s technical approach and focus on developer needs position it to address a growing infrastructure gap.
“Agent observability is a critical infrastructure layer for the next generation of AI, and Laminar has the architecture to support that role,” he said.
Growth Plans / Use Of Funds
Laminar will use the funding to accelerate product development and expand its go-to-market efforts. As adoption of AI agents increases, the company aims to scale its platform to support a broader range of use cases and enterprise requirements.
The company is also expected to continue refining its core features, including anomaly detection and debugging tools, while strengthening integrations with agent frameworks and developer ecosystems.
Since launching in 2025, Laminar has already been adopted by companies such as Browser Use, OpenHands, Rye.com, and Alai. Its software development kit is integrated into OpenHands’ agent infrastructure and is referenced as a default observability solution in Browser Use’s documentation.
About Laminar
Laminar is a developer infrastructure company building observability tools for AI agents. Founded in 2025, the company focuses on enabling developers to monitor, debug, and optimise complex AI workflows. Its platform captures detailed execution data across agent systems, providing visibility into decision-making processes and supporting reliable deployment at scale.
