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Agent Reliability

7 articles · Page 1 of 1
PaperAgent
PaperAgent
Sep 11, 2026 · Artificial Intelligence

Google's Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Google introduces Procedural Graphs, a self-evolving graph structure that explicitly encodes procedural knowledge for LLM agents, enabling them to locate, extract, and generate contextual guidance from a (procedure, relation, procedure) triplet graph, achieving 85% survival from 0% in CFO simulation and winning 21 of 24 model-benchmark combinations across 7 benchmarks and 4 LLMs.

Agent ReliabilityBenchmark EvaluationGraph-based Reasoning
0 likes · 9 min read
Google's Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
Xiaomi Tech
Xiaomi Tech
Sep 4, 2026 · Artificial Intelligence

Xiaomi's TRACE Method Wins IJCAI-ECAI 2026 CAR-bench Challenge with 70% Stable Pass Rate

Xiaomi's Darwin Agent team, collaborating with Nanjing University and Tsinghua University, won the IJCAI-ECAI 2026 CAR-bench Challenge Open Track with their TRACE method, achieving a 70% stable pass rate by decomposing experience into reusable actions, comparing execution traces to locate errors, and retrieving context-relevant experience for in-car voice assistant reliability.

Agent ReliabilityCAR-bench ChallengeIJCAI-ECAI 2026
0 likes · 10 min read
Xiaomi's TRACE Method Wins IJCAI-ECAI 2026 CAR-bench Challenge with 70% Stable Pass Rate
Design Hub
Design Hub
Sep 1, 2026 · Artificial Intelligence

How to Build an AI Agent That Won’t Fall Apart with Harness Engineering

The article explains that AI agents often fail because they lack a reliable runtime environment—called a Harness—and outlines a systematic Harness Engineering approach, including seven core responsibilities, a practical checklist, and concrete examples to turn failures into reusable infrastructure.

AI agentsAgent ReliabilityHarness Engineering
0 likes · 19 min read
How to Build an AI Agent That Won’t Fall Apart with Harness Engineering
AI Architecture Hub
AI Architecture Hub
Mar 15, 2026 · Artificial Intelligence

How OpenClaw Solves Long‑Task Context Challenges for AI Agents

This article analyses the real‑world pain points of long‑running AI agents, breaks down OpenClaw’s core concepts, explains its three‑layer context‑compression pipeline, presents four key engineering decisions, shares six practical techniques with essential parameters, and compares OpenClaw to competing approaches.

AI agentsAgent ReliabilityLLM Engineering
0 likes · 17 min read
How OpenClaw Solves Long‑Task Context Challenges for AI Agents
Instant Consumer Technology Team
Instant Consumer Technology Team
Nov 5, 2025 · Artificial Intelligence

Why AI Agents Fail: 70% Failure Rate & How Interleaved Thinking Improves Reliability

Recent CMU and Salesforce studies reveal that top‑tier AI agents like Gemini 2.5 Pro, Claude 3.7 Sonnet and GPT‑4o fail in 69‑70% of multi‑step tasks, but MiniMax‑M2’s Interleaved Thinking reduces failure dramatically, highlighting that execution mechanisms, not model size, are key to reliable AI agents.

Agent ReliabilityBenchmarkOpenAI API
0 likes · 17 min read
Why AI Agents Fail: 70% Failure Rate & How Interleaved Thinking Improves Reliability