Messy console logs hid why AI agents failed. Tracing call hierarchies let developers fix a prompt issue causing severe streaming delays.
An enterprise software platform developing action-based workflows, such as equipping 1,000 go-to-market employees at a $20 billion tech company with intelligent assistants.
Transitioning from simple conversational bots to complex agentic workflows created severe observability gaps, resulting in high latency and a...
“At Mendable.ai, we are seeing this transformation first hand.”
Platform for building AI chatbots trained on technical documentation and data.
Framework and developer platform for building LLM-powered applications.
Mendable's Application debugging is part of this use case:
Related implementations across industries and use cases
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Messy console logs hid why AI agents failed. Tracing call hierarchies let developers fix a prompt issue causing severe streaming delays.
An enterprise software platform developing action-based workflows, such as equipping 1,000 go-to-market employees at a $20 billion tech company with intelligent assistants.
Transitioning from simple conversational bots to complex agentic workflows created severe observability gaps, resulting in high latency and a...
“At Mendable.ai, we are seeing this transformation first hand.”
Platform for building AI chatbots trained on technical documentation and data.
Framework and developer platform for building LLM-powered applications.
Mendable's Application debugging is part of this use case:
Related implementations across industries and use cases
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.