Agentiqa vs LangSmith

Short answer: LangSmith, from LangChain, traces agents step by step and runs evals: offline on curated datasets during development, and online on production traffic. Agentiqa is an AI Quality System. Its release gates run your agent's key tasks before each release and check what changed in the connected system. LangSmith tells you how the agent reasons and how its outputs score. Agentiqa tells you whether the next version still writes the right records.

At a glance

"—" means we did not find it on the LangSmith pages listed in Sources. LangSmith facts checked on 31 Oct 2026.

What LangSmith does

LangSmith is LangChain's platform for building and running agents. Its observability and evaluation pages list:

• Tracing. Step-by-step traces of what the agent did, with SDKs for Python, TypeScript, Go and Java, native tracing for agent frameworks, and OpenTelemetry support.
• Offline evals. Run the agent on curated datasets during development to catch regressions before deployment.
• Online evals. Score production traffic with LLM-as-judge and code evaluators.
• Monitoring. Track tools and agent trajectories, costs and alerts, and cluster production traces into insights.

It works with LangChain and LangGraph, and with other frameworks through OpenTelemetry.

What Agentiqa does

Agentiqa is built for AI agents that write data into business systems: ERP and procurement suites, CRM, billing.

• Release gates run the agent's key tasks before each release. You describe each task in plain words. The gate runs it in a real browser with a test account and grades each check.
• The record, not only the reply. After each task, the gate checks what changed in the connected system.
• Confirmed regressions. When you rerun the failed tasks, AI Quality Manager reports a step as a regression only if it fails again. Unconfirmed failures are listed separately.
• AI Quality Manager compares each release with the previous one, shows regression candidates, and proposes test updates and new tests. A person on your team approves them.

Each failure comes with a trace, the recording and steps to reproduce.

Where they differ

LangSmith's offline evals and Agentiqa's release gates both run before deployment, so the difference is what they look at.

An offline eval scores outputs on a dataset: the answer, the tool chosen, the trajectory. A release gate runs a whole task with a test account and then reads the connected system. That is where these failures show up:

• Wrong data. The right record with a wrong value: a truncated ID, pieces instead of boxes, an amount in the tax field. See wrong data.
• A false "done". The agent reports success, but nothing was saved. See hallucinated success.
• Duplicate writes. A retry after a timeout creates a second order. See duplicate writes.

The trajectory for these cases can look correct. The record is where the error is.

When to pick which

• Pick LangSmith if you build on LangChain or LangGraph, or want tracing, dataset evals and production monitoring in one place.
• Pick Agentiqa if your agent changes records for customers and you want each release checked on its key tasks before it ships.
• Use both if you need both. LangSmith for development evals and production traces, Agentiqa for the release check on the tasks where a wrong write costs the most.

More on the split: evals vs testing for AI agents.

Sources

LangSmith: langchain.com/langsmith/observability, langchain.com/langsmith/evaluation, langchain.com/pricing, checked 31 Oct 2026. Agentiqa: agentiqa.com, Enterprise, Pricing.