TENESYS AI NEWS tracks the most important AI news and explains what changed, why it matters and whether a technology is worth testing.
“Claude” — 5 articles ✕
Anthropic rolled out a beta tracing feature that exports OpenTelemetry spans from both the Messages API and Claude Managed Agents sessions. Developers using the Claude SDK can now capture these spans natively in seven languages: Python, TypeScript, C#, Go, Java, PHP, and Ruby. Enabling the feature requires setting up a destination for the telemetry data inside the Claude Console.
NVIDIA published a case study showing how Siemens EDA applied its open NeMo Gym evaluation library to tighten up an agentic tool used for chip characterization. Instead of checking individual tool calls, the team scored complete final output files with a domain-specific verifier, then used the failure patterns to rewrite the agent's skill instructions and MCP tool descriptions. After several rounds of this loop, a composite accuracy benchmark climbed from roughly 55% to 95%, while the agent needed far fewer steps and far less output to get there. The experiments relied on about 25 evaluation rollouts running Claude Opus 4.8 as the agent model inside Claude Code.
Orca Security built Discovery AI, an Amazon Bedrock-based agent that turns plain-English security questions into structured queries against its Sonar system, which tracks over 900 cloud resource types. Rather than relying on one large model with the full schema stuffed into every prompt, the production version splits the work across nine specialized stages and multiple foundation models. The result: dramatically lower cost, faster responses, and better accuracy on complex queries, now running live since March 2026.
Anthropic has introduced a beta option on its Claude Platform that allows a Managed Agents agent to construct its own workflow for large, multi-part jobs, such as going through hundreds of documents. Instead of handling everything in one pass, the agent splits the task into phases, delegates pieces across multiple sub-agents, and merges their output into a single result. The job is handed off to the server, which executes it asynchronously and reports progress through a stream of events.
AWS published a joint engineering write-up with Postman describing how Agent Mode, Postman's AI-native interface for API testing and documentation, was built to run in production at scale. Rather than model quality, the team found that tool sprawl and missing context were the biggest obstacles to reliable agent behavior. The post details architectural patterns including dynamic tool scoping, schema-based query access, dedicated context handlers, and how Amazon Bedrock provides model flexibility, cross-Region inference, data residency controls, and prompt caching.