Interrupt 2026: LangChain unveils tools for debugging AI agents in production

LangChain held the two-day Interrupt 2026 conference, where it introduced new tools for diagnosing and debugging AI agents in production and showed real-world examples from LinkedIn, Rippling, Cisco and 19 other large companies.

Three tools for production teams

At the conference, LangChain announced three products built specifically for teams that ship agents into live systems:

• LangSmith Engine — a platform for error diagnosis. It shows a full trace of the call chain: which LLM gave a wrong answer, at which step context was lost, which tool returned an unexpected result. This matters because real agents consist of dozens of steps, and a single error at step 7 can wreck the entire outcome.
• Sandboxes GA — a separate isolated environment for testing, now generally available (GA). Teams can experiment with new agent versions, land changes safely and roll them back without risking production. It is especially useful for A/B tests: which LLM version performs better, which prompt is more effective.
• LangChain Labs — a closed space for experimenting with beta features. LangChain developers post fresh ideas there: new retry strategies, context caching approaches, experimental routing mechanisms. Production teams can adopt them early and give developers direct feedback.

Examples from real-world development

The conference featured 23 speakers — senior engineers from LinkedIn, Rippling, Cisco and 19 other companies.

LinkedIn described a recommendation agent that suddenly started ignoring the geolocation filter. The agent was convinced that a candidate living in India was a fit for a position in San Francisco. The problem turned up in LangSmith Engine: at the filtering step, a tool returned data in the wrong format, and the LLM did not notice the contradiction. After debugging, the agent works correctly.

Rippling showed how it built out cost control for API calls. One production agent cost the company $200 a day in LLM calls. After the team introduced context caching and simplified its prompts, the cost fell to $120 a day. Over a month, that saves $2400 on a single agent.

Cisco talked about the main pain point of debugging in a microservice architecture. When an agent consists of a dozen different services (one calls a CRM, another a REST API, a third S3) and the chain breaks at service 6, it becomes a real nightmare. Logging to LangSmith is not enough; traces also have to be synchronized across services. Cisco showed how it solved this pattern with OpenTelemetry.

What this means

Production AI agents are a new frontier of engineering, and the old monitoring stack does not fit them. LangChain is investing in debugging tools because this has become a critical bottleneck for the whole industry.

All conference videos are available online on demand.