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Closing the Feedback Loop: How Evaluation Metrics Prevent AI Agent Failures

Closing the Feedback Loop: How Evaluation Metrics Prevent AI Agent Failures

TL;DR AI agents often fail in production due to tool misuse, context drift, and safety lapses. Static benchmarks miss real-world failures. Build a continuous feedback loop with four stages: detect (automated evaluators on production logs), diagnose (replay traces to isolate failures), decide (use metrics and thresholds for promotion gates)

Agent Evaluation for Multi-Turn Consistency: What Works and What Doesn’t

Agent Evaluation for Multi-Turn Consistency: What Works and What Doesn’t

TL;DR: Multi-turn AI agents need layered evaluation metrics to maintain consistency and prevent failures. Successful evaluation combines session-level outcomes (task success, trajectory quality, efficiency) with node-level precision (tool accuracy, retry behavior, retrieval quality). By integrating LLM-as-a-Judge for qualitative assessment, running realistic simulations, and closing the feedback loop between testing

From Black Box to Glass Box: Achieving Transparency with AI Observability

From Black Box to Glass Box: Achieving Transparency with AI Observability

Modern AI systems (LLM-powered chatbots, RAG pipelines, and autonomous agents) often feel opaque. Teams wrestle with questions like “Why did the agent make that decision?” or “Which prompt or retrieval step caused the failure?” Moving from a black box to a glass box demands systematic instrumentation across the AI stack,

The Technical Guide to Managing LLM Costs: Strategies for Optimization and ROI

The Technical Guide to Managing LLM Costs: Strategies for Optimization and ROI

LLM costs have become a critical concern for engineering teams deploying AI applications at scale. In 2025, API pricing ranges from $0.25 to $15 per million input tokens and $1.25 to $75 per million output tokens, creating significant budget variability depending on model selection and usage patterns. Organizations

Solving the 'Lost in the Middle' Problem: Advanced RAG Techniques for Long-Context LLMs

Solving the 'Lost in the Middle' Problem: Advanced RAG Techniques for Long-Context LLMs

TLDR: Long-context LLMs often miss information placed mid-sequence (“lost in the middle”), driven by positional biases like RoPE decay. Production fixes: two-stage retrieval (broad recall + cross-encoder reranking), hybrid search (semantic + BM25), and strategic ordering (top evidence at start and end). Strengthen chunking with contextual retrieval; keep only the most relevant

How to Stress Test AI Agents Before Shipping to Production

How to Stress Test AI Agents Before Shipping to Production

TL;DR AI agents are failing in production at alarming rates, with over 40% of projects expected to be canceled by 2027 due to inadequate testing and unclear business value. Recent benchmarks show frontier models failing basic tasks up to 98% of the time. This article explores why traditional testing

Designing Evaluation Stacks for Hallucination Detection and Model Trustworthiness

Designing Evaluation Stacks for Hallucination Detection and Model Trustworthiness

TL;DR Building trustworthy AI systems requires comprehensive evaluation frameworks that detect hallucinations and ensure model reliability across the entire lifecycle. A robust evaluation stack combines offline and online assessments, automated and human-in-the-loop methods, and multi-layered detection techniques spanning statistical, AI-based, and programmatic evaluators. Organizations deploying large language models need