AI Agents Do Not Fail Alone:The Context Fails First
Fouad Bousetouane
Why It Matters
What makes this one worth your time
Understanding and measuring context quality can significantly enhance the reliability and governance of AI agents, making this research relevant for both developers and researchers in the field.
Context quality is a key predictor of AI agent reliability.
Summary
The paper introduces a framework for measuring context quality in AI agents, demonstrating that context engineering is a critical factor influencing agent reliability and behavior across various domains.
Key contributions
- Validation of context-engineering quality as a leading indicator of agent reliability.
- Development of ProofAgent-Harness for context evaluation with specific scoring criteria.
- Empirical evidence linking context quality to behavioral outcomes in AI agents.
Notable insights
- The study isolates context quality from behavioral metrics, allowing for a non-circular validation of agent reliability.
- The use of multi-juror, consensus-based scoring in the ProofAgent-Harness provides a novel approach to evaluating context across multiple criteria.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.14275v1 Announce Type: new Abstract: Context engineering has become central to building reliable AI agents, yet it remains largely unmeasured. Agents do not fail in isolation: their behavior is shaped by the instructions, tools, memory, retrieved knowledge, guardrails, and untrusted inputs accumulated in their context. When this context is weak, agents drift, hallucinate, misuse tools, ignore constraints, become vulnerable to injection, and waste tokens. This paper validates context-engineering quality as an independent leading indicator of agent reliability. We implement the measurement in ProofAgent-Harness, an open-source infrastructure for AI agent evaluation that uses multi-juror, consensus-based scoring. The harness assesses context across seven criteria: role clarity, guardrail coverage, instruction consistency, tool schema quality, grounding sufficiency, injection hardening, and token efficiency. Crucially, the context score is isolated from behavioral metrics and release decisions, enabling a non-circular validation. Through a controlled context-quality study across regulated agent domains, holding frontier LLM agents fixed and varying only their operating context, we show that context-quality criteria consistently predict their corresponding behavioral outcomes. Grounding sufficiency predicts hallucination resistance, guardrail coverage predicts manipulation resistance, instruction consistency predicts instruction following, and tool-schema quality predicts tool use. These findings establish context measurement as a validated preflight signal for agent reliability and position context engineering as an auditable layer of agent evaluation and governance.