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.
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.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
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.
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.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.