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“[Atla’s] real-time data crunching and presentation provides immediate insights that would otherwise require significant upfront planning and analysis time.
This helps us spot failures modes of our agent prompts in days instead of weeks.”
Background
Trusted by patent attorneys at top firms in 15+ countries, ClaimWise is building the first AI agent for patent prosecution work, serving patent attorneys with a sophisticated multi-agent system:
- Routing agents that direct queries to analysis agents
- Analysis agents that evaluate patentability and legal arguments across multiple document sources
- Worker agents that crunch through prior art and relevant case law
- Specialized extraction agents that parse and structure information from complex legal documents
The Challenge
As ClaimWise's multi-agent system grew in complexity, the team faced several critical challenges:
1) Drowning in traces:
With hundreds of thousands of logs each month, manually reviewing traces to understand agent behavior was drowning them in noise.
2) Opportunities for agent improvements buried in data:
While certain errors occurred frequently, surfacing them and translating them into specific improvements was time-intensive. The team needed to understand not just what was failing, but which specific agents required attention and what changes would have the highest impact.
""The alternative to Atla would be to hire a team of experienced evals engineers.""
3) Bottlenecks in the product release cycle:
Without systematic evaluation, the team found it time-consuming to assess whether prompt / architectural changes improved performance or introduced regressions elsewhere in the system. This created a bottleneck that prevented rapid iteration.
The Result: Debugging and Improving Agents with Atla
How ClaimWise transformed their development process:
1) Effortless setup:
ClaimWise was up and running Atla seamlessly without disrupting their existing codebase.
“The setup is incredibly, incredibly simple. It was just a wrapper over the function.”
2) Immediate system-level improvements:
Within a week of implementation, ClaimWise discovered and shipped 6 high-impact improvements around:
- Data format inconsistencies in extraction processes
- Agents making suggestions without proper user validation flows
- Context retrieval inefficiencies where agents did not rank and prioritize source materials effectively
- Incomplete data extraction that required pipeline restructuring
- Reasoning errors where agents sometimes did not follow domain-specific instructions
- Workflow inefficiencies where agents performed unnecessary operations
Patent attorneys using ClaimWise now experience more reliable outputs, directly improving their day-to-day legal work.
3) Supercharged development workflow:
Rather than getting lost analyzing individual traces from the start, ClaimWise now uses Atla's Error Patterns to immediately identify which agents need attention and what fixes will have the biggest impact. This systematic approach gives the team confidence to iterate and ship quickly.
“The way we’re using it today is we try to go through every few days starting with the patterns and then going to the detailed traces... based on that, if I see a few common issues by agents, we create a ticket on that... and then we do that every few days.”
By adopting Atla, ClaimWise transitioned from reactive debugging to proactive system optimization, maintaining quality while scaling their sophisticated agent workflows.
Book a demo call with us below to start improving your agent.
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