Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
A cybersecurity startup building autonomous digital employees that execute complex identity and access management workflows, managing millions of identities across dozens of enterprise customers.
As the multi-agent architecture scaled, abstraction layers hid prompts, reasoning paths, and retries behind a black box. This lack of visibility...
“Datadog LLM Observability gives us complete visibility into our agents’ reasoning. We stopped guessing. We can see the prompt, the retries, the tool calls, and the cost of every step.”
AI digital employees for automated identity security and governance.
Observability and security platform for cloud-scale monitoring and analytics.
Twine Security's Model monitoring is part of this use case:
Related implementations across industries and use cases
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.
AI costs visible only in aggregate—regressions lurked for 24 hours. Observability cut detection to under an hour, saving $280K annually.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
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.
AI costs visible only in aggregate—regressions lurked for 24 hours. Observability cut detection to under an hour, saving $280K annually.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
A cybersecurity startup building autonomous digital employees that execute complex identity and access management workflows, managing millions of identities across dozens of enterprise customers.
As the multi-agent architecture scaled, abstraction layers hid prompts, reasoning paths, and retries behind a black box. This lack of visibility...
“Datadog LLM Observability gives us complete visibility into our agents’ reasoning. We stopped guessing. We can see the prompt, the retries, the tool calls, and the cost of every step.”
AI digital employees for automated identity security and governance.
Observability and security platform for cloud-scale monitoring and analytics.
Twine Security's Model monitoring is part of this use case:
Related implementations across industries and use cases
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.
AI costs visible only in aggregate—regressions lurked for 24 hours. Observability cut detection to under an hour, saving $280K annually.
Manually tuning prompts in secure environments was slow and inaccurate. Now, automated feedback loops let engineers refine AI instantly.
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.
AI costs visible only in aggregate—regressions lurked for 24 hours. Observability cut detection to under an hour, saving $280K annually.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
Keyword bots bottlenecked 100 agents supporting millions. Now, AI resolves FAQs, freeing staff to mine chat logs for product feedback.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.