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Why Deep Research Agents Fail: Lessons from GAIA

Find and fix AI agent failures

The agent observability and evals platform that dives deeper. Automatically detect errors and get insights to continually improve agent performance.
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Trusted by the Best. Backed by the Best.
Microsoft, Y Combinator, CreandumCohere, Meta, Deepmind

Clarity for the
agentic age.

Trace every step, identify root causes of failures, and improve completion rates–with a platform designed to clarify the messiness of agent behaviour.
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Monitoring Agents
Your agents are live. Anything can go wrong. Atla gives you real-time visibility into every thought, tool call, and interaction.
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Identifying Patterns
Stop manually combing through traces. Atla automatically surfaces recurring issues across thousands of traces and gives you suggestions for improvement.
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Running Experiments
Experiment with models and prompts. Compare performance side by side to know what works and what doesn’t.

Actionable suggestions to fix your agent

Get suggestions for how to fix your agents. Suggestions are specific and actionable, and are based on analysis of error patterns across all your traces.
See how suggestions work
Example of a suggested fix from a pattern detected by Atla
Example of a pattern detected by Atla

Find the error pattern, not just the bug

Save time manually reviewing traces and doing vibe checks. Easily understand error patterns across thousands of traces, and focus on fixing them from the root.
See how patterns work

Gain a granular understanding with individual traces

Review agent runs that have been summarized into clean, readable narratives. Understand all the actions your agent takes, and zoom in on specific, step-level errors.
Start tracing your agent
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Example of the Atla experiments feature page

Run experiments across all criteria

Compare prompts and models side-by-side. Implement improvement suggestions and understand how changes affect performance.
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Works with the tools you already use

Plug Atla into your existing stack in minutes.
crew ai, agno, openai, langchain, smolagents, autogen ai, pydantic aicrewai, agno, openai, langchain, smolagents, autogenAI, pydanticAI

Latest from the Lab

Research and stories from the team fixing agents at scale.
Why do AI browser or deep research agents fail? This deep dive into the GAIA benchmark reveals the most common failure modes in browser agents, from planning errors to missing tool calls. Additionally, explore how human feedback boosts agent performance by 20%. Read more to understand how to debug AI agents.
Why Deep Research Agents Fail: Lessons from GAIA
Sashank
July 10, 2025
Explore and compare the top AI agent frameworks, including LangGraph, AutoGen, CrewAI, Agno, LlamaIndex, Semantic Kernel, and SmolAgents, plus OpenAI and Google SDKs. Learn which is best for your use case, and how to evaluate and monitor agents in production.
Comparing AI Agent Frameworks: A Guide to Building Reliable Agents
Kyle
June 30, 2025

See why your agents fail.

Install the Atla package
Track your agents
Understand errors instantly
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