Agents looked healthy — 200s, no errors — while decisions quietly deteriorated. Unified tracing across every layer now surfaces the why.
A global IT services firm whose Technology Innovation Headquarters validates emerging AI architectures and develops the operational frameworks that enterprise customers use to move agentic AI from experimentation into production.
Traditional application monitoring could confirm agents were running but couldn't explain how they reached decisions: which prompt drove a specific...
“Many organizations are exploring how to use AI, but concerns around quality, governance, security, and operational monitoring often slow adoption.”
Global IT services and consulting provider for digital transformation and cloud.
Observability and security platform for cloud-scale monitoring and analytics.
NTT DATA's Agent monitoring is part of this use case:
Related implementations across industries and use cases
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
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.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
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.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
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.
Agents looked healthy — 200s, no errors — while decisions quietly deteriorated. Unified tracing across every layer now surfaces the why.
A global IT services firm whose Technology Innovation Headquarters validates emerging AI architectures and develops the operational frameworks that enterprise customers use to move agentic AI from experimentation into production.
Traditional application monitoring could confirm agents were running but couldn't explain how they reached decisions: which prompt drove a specific...
“Many organizations are exploring how to use AI, but concerns around quality, governance, security, and operational monitoring often slow adoption.”
Global IT services and consulting provider for digital transformation and cloud.
Observability and security platform for cloud-scale monitoring and analytics.
NTT DATA's Agent monitoring is part of this use case:
Related implementations across industries and use cases
Legacy migrations took 12.5 man-months. Engineers now set the architecture and let AI handle bulk coding, freeing them to focus on QA.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
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.
Black-box AI logic hid costly retry loops. Granular traces exposed redundant tool calls, enabling engineers to optimize agent reasoning.
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.
Manually updated profiles left skills stale. The first prototype took two hours per 1,000 consultants—now the agent covers 130K in minutes.
Routine cloud setups tied up engineers for days. Now, developers ask AI agents in Jira and Webex to provision resources instantly.
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.