Enterprise AI Teams Are Turning To HoneyHive For Reliability

In October 2023, Mohak Sharma and Dhruv Singh started HoneyHive with a simple but urgent premise: building AI-powered products that actually work in the real world requires more than access to large language models. The AI boom had triggered a wave of experimentation, but teams quickly hit a wall when prototypes failed in production. Debugging non-deterministic models, tracking failure modes across multi-agent systems, and iterating with confidence remained unsolved problems for most companies.

Just over a year later, HoneyHive is launching out of beta with $7.4 million in funding to address these issues head-on. The round includes a $5.5 million Seed led by Insight Partners, along with a previously unannounced $1.9 million Pre-Seed led by Zero Prime Ventures. Additional investors include AIX Ventures, 468 Capital, MVP Ventures, and Firestreak Ventures. Notable angel investors such as Jordan Tigani (CEO at Motherduck) and Savin Goyal (CTO at Outerbounds) also participated. George Mathew, Managing Director at Insight Partners, has joined HoneyHive’s board of directors.

The funding follows significant momentum during the company’s beta period, where HoneyHive logged over 50x growth in AI requests through its platform in 2024 alone and doubled its team size. The company now plans to accelerate product development and expand its enterprise offerings to meet growing demand.

At its core, HoneyHive is an AI agent observability and evaluation platform designed to help teams build more reliable LLM-based systems. It was built on the recognition that traditional software development lifecycles and DevTools aren’t suited for AI. “Enterprise leaders find it hard to trust LLMs in mission-critical workflows. Models hallucinate, agents loop, and RAG pipelines constantly fail,” said Sharma. “Even with a working prototype in hand, teams struggle to gain confidence in the safety and reliability of their LLM applications.”

HoneyHive addresses this by offering end-to-end evaluation throughout the AI lifecycle from early development through production. It allows teams to run structured evaluations using LLMs, code, and human feedback to surface edge cases and performance regressions before systems go live. Once in production, the platform captures real-world agent behavior using OpenTelemetry-based observability, integrating with existing monitoring stacks and making it easier to understand what agents are doing, where they’re failing, and why.

Crucially, HoneyHive closes the loop between development and production. When failures occur in the wild, they’re automatically captured and turned into test cases, giving teams a concrete path to improve. The platform also acts as a collaborative system of record—versioning traces, prompts, tools, datasets, and evaluators—to help teams track system evolution and maintain compliance in regulated environments.

“The transition from experimental AI agents to production-ready systems requires a fundamental shift in how we approach evaluation and monitoring,” Sharma said. “Our GA release builds on the lessons learned from our beta customers, delivering a comprehensive platform that addresses the challenges of complex agent architectures.”

Those customers span from AI-first startups to Fortune 100 companies in industries like insurance and financial services. One team saw a 340% improvement in agent accuracy on web-browsing tasks after adopting HoneyHive. Another enterprise accelerated its development cycle by 5x across multiple business units by relying on HoneyHive’s evaluation and observability capabilities to validate new agents before rollout.

According to Mathew from Insight Partners, the platform’s ability to leverage traces for monitoring and evaluation gives it a critical role in the emerging enterprise AI stack. “Enterprise AI agents are evolving from performing simple tasks to becoming the building blocks of sophisticated AI systems,” he said. “HoneyHive’s approach plays a critical role in the enterprise AI stack. The team’s execution and deep technical expertise positions us well in this segment of the observability market.”

With general availability, HoneyHive is rolling out new enterprise-grade features including advanced offline evaluation frameworks for testing complex agent interactions, OpenTelemetry-based monitoring, and self-hosted deployment options for regulated industries. The platform also introduces systematic detection of edge cases and failure modes in multi-agent systems.

CTO Dhruv Singh summed up the company’s mission clearly: “Enterprises are struggling to bridge the gap between AI agent prototypes and production-ready systems. By closing the loop between development and production monitoring, we help companies systematically evaluate their AI agents, catch failure modes early, and continuously improve performance based on real-world data.”