Stop Blaming the Model: Decomposition, Not Model Tier, Is What Makes an Agent Production-Ready
Every few months another team tells me the same story. They built an agent on a frontier model, it worked beautifully in the demo, and...
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Every few months another team tells me the same story. They built an agent on a frontier model, it worked beautifully in the demo, and...
Shadow AI is no longer just an employee opening ChatGPT without telling IT. Employees are using coding agents with personal API keys. Teams are connecting...
Your security team says traces can’t leave the building. Your finance team says the observability bill can’t grow with traffic. Your engineering team says they...
Your AI governance policy is a PDF. The agent that just approved the payment never opened it, and the dashboard that flagged the problem did...
AI agent guardrails are relatively straightforward to evaluate when an agent only generates text. The decision gets more complicated as soon as the agent can...
A support agent reads a customer’s attached PDF. Buried in white text on page four is a sentence the customer never typed and your user...
The dashboard is green. The API returned 200, latency is normal, and no infrastructure alert fired. Meanwhile, the agent chose the wrong tool, looped four...
Your APM can tell you a request returned HTTP 200 in 900 milliseconds. It can’t tell you why the model hallucinated, which document the retriever...
An internal ops agent gets a simple task: reconcile last month’s invoicing discrepancies. One record keeps failing validation. So the agent retries again and again,...
Picking the best tools for AI agent session logging tends to happen after an incident, not before. A support agent issues the wrong refund on...
Testing an AI agent once isn’t enough. An agent can pass a few happy-path checks and still fail when a user changes direction, a tool...
An LLM response can fail for many reasons: a bad prompt, the wrong retrieved document, a slow model call, an unexpected tool call, a routing...
Your agent passed every test in the demo. Then someone renamed a field in the order API, and the cancellation workflow started confirming refunds it...
Quick answer: MCP security risks in 2026 center on tool poisoning, rogue or unauthenticated MCP servers, over-scoped permissions, and unmanaged tool lifecycles. As agents gain...
Key takeaways An LLM starts generating its first token in under 500 milliseconds. The client is built to render tokens as they arrive. And the...
Your support widget looks harmless enough. It’s wired to an agent with tool access to your refund system, sitting on a public page anyone can...
Your auditor asks which AI agents accessed customer data last quarter, who approved each one into production, and what guardrail violations were flagged. If the...
You’re here because you already know you need to monitor your AI agents. Maybe you’ve looked at LangSmith, Langfuse, Arize, or Braintrust, and now you’re...
Picking the best tools for LLM evaluation in enterprise settings has stopped being a research exercise. It’s now a line item a buying committee has...
Most teams testing prompt injection start with the same question: What happens if someone gives the agent a malicious prompt? That test matters. But it...
Compare the 11 best LLM tracing and debugging tools for 2026: Lyzr Open Controller, Langfuse, LangSmith, Arize Phoenix, Braintrust, MLflow, W&B Weave, Opik, OpenLLMetry, Datadog and Splunk, scored on trace depth, cost control, data...
You already know what agent failure detection is. The harder part is deciding which platform actually fits your stack. Some tools focus on tracing individual...
Tool sprawl in agentic AI happens when the number of tools, APIs, and integrations connected to autonomous agents grows faster than anyone can track them....
A model passes every offline eval in CI. You ship. Production breaks within a week, because a class of prompts your eval dataset never exercised...