AI Agents Archives » Page 2 of 4 » Alpesh Kumar

Build an AI Agent Evaluation Flywheel That Improves Prompts — a text-free circular technical schematic shows recorded agent traces being separately evaluated for outcomes and trajectories, classified into recurring failure types, turned into prompt candidates, and checked against development, hold-out, and regression cases before feeding evidence back into the next run. Visual direction: idea: a trace-aware evaluation loop turns recurring agent failures into verified prompt changes; subject: AI agent evaluation flywheel; mechanism: captured tool traces flow through separate outcome and trajectory evaluation, failure taxonomy, prompt variants, and hold-out/regression verification with a safety gate; composition: a single clockwise five-stage circular schematic with evidence records at the top and a verification boundary closing the loop; palette: warm off-white, graphite, deep forest green, muted rust, clay orange, restrained golden yellow; exclusions: dark navy, neon-blue glow, floating UI cards, network nodes, generic AI circuitry, people, robots, logos, readable artwork text.

AI Agents, Artificial Intelligence

Build an AI Agent Evaluation Flywheel That Improves Prompts

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Why Final-Answer Evals Leave AI Agent Failures Invisible — a warm editorial execution-trace schematic contrasts a reassuring green final-response indicator with the visible failed path beneath it: wrong tool selection, a quoted-number parameter mismatch, and a missing update state, all examined by a trajectory evaluator. Visual direction: idea: an apparently successful answer hides a failed execution path; subject: AI-agent trajectory evaluation; mechanism: evaluator compares final claim with recorded tool selection, typed arguments, missing action, and state transitions; composition: left-to-right audit strip beneath a detached green outcome capsule with an inspection bracket; palette: warm ivory, charcoal, muted sage, terracotta, ochre; exclusions: dark navy, neon-blue glow, floating UI cards, network nodes, generic AI circuitry, people, robots, logos, readable artwork text.

AI Agents, Artificial Intelligence, Software Architecture

Why Final-Answer Evals Leave AI Agent Failures Invisible

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Multi-Agent Systems: Your Guided Learning Path — a visible connected route through distinct AI-workflow stations, guardrails, and a human-reviewed destination represents the article’s structured learning path from foundations to responsible decisions.

AI Agents

Multi-Agent Systems: Your Guided Learning Path

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Context Engineering for AI Agents: Memory, Retrieval, and Token Budgets — an abstract AI-agent core receives separately filtered memory, retrieved evidence, and segmented token-budget streams, representing deliberate context selection for the next decision.

AI Agents, Artificial Intelligence, LLM, Software Architecture

Context Engineering for AI Agents: Memory, Retrieval, and Token Budgets

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