Agent failures at 1M ops/day meant engineers stitching logs, traces, and code to diagnose. Now every decision links to a commit in one view.
A software platform running roughly one million LLM-powered operations daily, with autonomous agents that review, investigate, and fix software changes directly in production codebases across large engineering environments.
The team had strong telemetry across logs and traces but no unified way to understand how agents reasoned through decisions. When an agent produced...
“Our goal is to make our autonomous agents safe to run in production. That means every decision has to be transparent, explainable, and grounded in real context.”
AI coding agents that operate, review, and secure codebases.
Observability and security platform for cloud-scale monitoring and analytics.
Baz's Code review agents is part of this use case:
Related implementations across industries and use cases
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Reviewers struggled to predict how code ripples through the system. AI now flags cross-service risks that cause outages.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.
Agent failures at 1M ops/day meant engineers stitching logs, traces, and code to diagnose. Now every decision links to a commit in one view.
A software platform running roughly one million LLM-powered operations daily, with autonomous agents that review, investigate, and fix software changes directly in production codebases across large engineering environments.
The team had strong telemetry across logs and traces but no unified way to understand how agents reasoned through decisions. When an agent produced...
“Our goal is to make our autonomous agents safe to run in production. That means every decision has to be transparent, explainable, and grounded in real context.”
AI coding agents that operate, review, and secure codebases.
Observability and security platform for cloud-scale monitoring and analytics.
Baz's Code review agents is part of this use case:
Related implementations across industries and use cases
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Reviewers struggled to predict how code ripples through the system. AI now flags cross-service risks that cause outages.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.