--- title: Building a Multifaceted Agentic AI Evaluation Framework... description: Two Six Technologies describes building an evaluation framework for Helix, its agentic AI orchestrator used in national security settings. The framework... canonical: https://daily.dev/posts/building-a-multifaceted-agentic-ai-evaluation-framework-for-helix-ylejxf1u7 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Building a Multifaceted Agentic AI Evaluation Framework for Helix | daily.dev og:description: Two Six Technologies describes building an evaluation framework for Helix, its agentic AI orchestrator used in national security settings. The framework... og:url: https://daily.dev/posts/building-a-multifaceted-agentic-ai-evaluation-framework-for-helix-ylejxf1u7 og:image: https://api.daily.dev/og/posts/YLeJxf1u7.png og:image:alt: Building a Multifaceted Agentic AI Evaluation Framework for Helix og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Building a Multifaceted Agentic AI Evaluation Framework for Helix **[Two Six Technologies](https://daily.dev/sources/twosixtech)** · 5 min read · 0 upvotes · 0 comments ## Summary Two Six Technologies describes building an evaluation framework for Helix, its agentic AI orchestrator used in national security settings. The framework combines a trace-and-check test harness (originally built for a system called Sentr) with a domain-specific evaluation suite that benchmarks LLMs, workflows, and prompt strategies. Rather than simple pass/fail checks, it tracks latency, token usage, cost, citation quality, and tool-call behavior over time to detect performance drift. Future plans include integrating these checks into CI/CD pipelines and adding live monitoring via Langfuse and Kibana to catch degradation before it affects users. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do you evaluate the reliability of an agentic AI system that orchestrates multiple LLM agents? A trace-and-check model captures prompts, responses, and tool behavior for each run, then applies checks for tool use, remote-agent delegation, and citation grounding, with optional LLM-judge scoring for semantic quality. Beyond pass/fail, aggregated metrics track answer rate, latency, tool-call behavior, and token/cost summaries to detect drift over time rather than judging a single response in isolation. _daily.dev surfaces this kind of evaluation-architecture writeup for teams designing reliability checks into multi-agent systems._ ### Why is pass/fail testing insufficient for evaluating LLM-based agent systems? Because LLM behavior is non-deterministic, a test run can pass while still drifting in latency, token usage, or cost, so binary outcomes alone miss gradual performance degradation. Tracking broader quality metrics over time, alongside per-case pass/fail results, reveals whether a system's behavior stays stable across changing conditions rather than just looking correct once. _Teams hardening agent pipelines against silent drift can follow this reasoning through daily.dev._ ## Similar posts on daily.dev - [Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned](https://daily.dev/posts/evaluating-ai-agents-in-practice-benchmarks-frameworks-and-lessons-learned-tohpewvfd) · InfoQ · 2 upvotes · 0 comments - [Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100\+ Deployments](https://daily.dev/posts/building-an-evaluation-harness-for-production-ai-agents-a-12-metric-framework-from-100-deployments-2f5afv6v6) · Towards Data Science · 0 upvotes · 0 comments - [Evaluating AI agents: Real-world lessons from building agentic systems at Amazon](https://daily.dev/posts/evaluating-ai-agents-real-world-lessons-from-building-agentic-systems-at-amazon-lkttxw05m) · AWS · 2 upvotes · 0 comments - [AgentX - Multi-agent and eval framework: Build and evaluate multi AI agents with any LLM](https://daily.dev/posts/agentx---multi-agent-and-eval-framework-build-and-evaluate-multi-ai-agents-with-any-llm-wehqx6hqe) · Product Hunt · 0 upvotes · 0 comments - [Top Agentic Frameworks for Building Applications 2026](https://daily.dev/posts/top-agentic-frameworks-for-building-applications-2026-3nnimzjhl) · JetBrains · 2 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#cicd](https://daily.dev/tags/cicd), [#observability](https://daily.dev/tags/observability), [#agentic-ai](https://daily.dev/tags/agentic-ai) [View this post on daily.dev](https://daily.dev/posts/building-a-multifaceted-agentic-ai-evaluation-framework-for-helix-ylejxf1u7) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Building a Multifaceted Agentic AI Evaluation Framework for Helix","url":"https://daily.dev/posts/building-a-multifaceted-agentic-ai-evaluation-framework-for-helix-ylejxf1u7","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/building-a-multifaceted-agentic-ai-evaluation-framework-for-helix-ylejxf1u7"},"datePublished":"2026-08-18T00:25:06.224Z","dateModified":"2026-09-13T21:09:53.921Z","description":"Two Six Technologies describes building an evaluation framework for Helix, its agentic AI orchestrator used in national security settings. 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