Quick Facts
- LangChain CEO Harrison Chase says ‘harness engineering’ is crucial for AI agent production success
- Quality issues remain the top barrier, affecting 32% of organizations deploying AI agents
- LangChain raised $125 million at $1.25 billion valuation and works with 35% of Fortune 500 companies
LangChain co-founder and CEO Harrison Chase argues that better AI models alone won’t solve the production problems plaguing AI agents. Instead, he emphasizes ‘harness engineering’ as the key to moving agents from prototype to real-world deployment.
‘When agents mess up, they mess up because they don’t have the right context; when they succeed, they succeed because they have the right context,’ Chase said. He defines context engineering as ‘bringing the right information in the right format to the LLM at the right time.’
Chase’s argument comes as industry data shows a stark production reality. While 57% of organizations have agents in production, quality remains the biggest barrier for 32% of companies. This represents a persistent challenge from previous years, encompassing accuracy, relevance, consistency, and brand adherence.
The LangChain CEO advocates for a new approach where large language models control their own context engineering. ‘Rather than hard code everything into one big system prompt, you could have a smaller system prompt,’ Chase explained. ‘This is the core foundation, but if I need to do X, let me read the skill for X. If I need to do Y, let me read the skill for Y.’
LangChain has emerged as a major player in the AI infrastructure space. The company announced a $125 million Series B funding round at a $1.25 billion valuation in October 2025. The company now works with 35% of Fortune 500 companies and processes over 1 billion events per day on its LangSmith platform.
Chase also commented on OpenAI’s acquisition of OpenClaw, questioning whether the deal actually helps OpenAI build safer enterprise products. OpenClaw gained viral success by being willing to ‘let it rip’ in ways major labs typically avoid, according to Chase.
The broader industry faces significant production challenges in 2026. Nearly two-thirds of organizations experiment with AI agents, but fewer than one in four have successfully scaled them to production. Legacy agents suffer a 90% failure rate within weeks of deployment due to inadequate architectural depth.
Technical infrastructure requirements have evolved rapidly. About 89% of organizations now implement observability for their agents, while 52% have adopted evaluation systems. Among companies with production agents, 94% use some form of observability and 71.5% have full tracing capabilities.
Read more: LangChain’s CEO argues that better models alone won’t get your AI agent to production
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