Interrupt 2026: LangChain Unveiled Tools for Debugging AI Agents in Production

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

Three Tools for Production Teams

At the conference, LangChain announced three products developed specifically for teams that deploy agents to production systems:

• LangSmith Engine — a platform for error diagnostics. It shows full call chain tracing: which LLM gave the wrong answer, at which step context was lost, which tool returned an unexpected result. This is critical because real agents consist of dozens of steps, and a single error at step 7 can completely break the outcome.

• Sandboxes GA — a separate isolated environment for testing. Now in general availability (GA). Teams can experiment with new agent versions, safely land changes and roll back without risking breaking production. Especially useful for A/B testing: which LLM version works 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 methods, experimental routing mechanisms. Production teams can adopt them early and give direct feedback to developers.

Examples from Production Development

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

LinkedIn told the story of a recommendation agent that suddenly started ignoring the geolocation filter. The agent was convinced that a candidate living in India was suitable for a position in San Francisco. The problem was found in LangSmith Engine: at the filtering step, the tool was returning data in the wrong format, and the LLM failed to notice the contradiction. After debugging, the agent works correctly.

Rippling demonstrated how they piloted cost control for API calls. In a day, one production agent cost the company $200 in LLM calls. After implementing context caching and simplifying prompts, the cost dropped to $120 per day. Over a month, this is a savings of $2,400 on a single agent.

Cisco discussed the main pain point with debugging in microservice architecture. When an agent consists of a dozen different services (one calls CRM, another calls REST API, a third calls S3), and the chain breaks at service 6 — it becomes a real nightmare. You need to log not only in LangSmith but also synchronize traces across services. Cisco showed how they solved this pattern through OpenTelemetry.

What This Means

Production AI agents are a new frontier in engineering, and the old monitoring stack doesn't work for them. LangChain is investing in debugging tools because this has become a critical bottleneck for the entire industry. All conference videos are available online upon request.