agent-constraint-triangle-scorer

Agent Constraint Triangle Scorer

Overview

Ch1 names a fundamental challenge in building agents: the agent constraint triangle, "three interconnected constraints that create an inherently difficult operational problem":

1. Complexity management — multistep planning and reasoning. "As tasks require more steps and deeper analysis, cognitive load increases exponentially," producing "compounding errors as the step count increases."
2. Tool orchestration — translating natural language into precisely structured API calls. The named failure mode is "bloated tool sets that cover too much functionality or lead to ambiguous decision points about which tool to use." Anthropic's principle: "If a human engineer can't definitively say which tool should be used in a given situation, an AI agent can't be expected to do better."
3. Context utilization — organizing a fixed context window, "the model's attention budget." Chroma's needle-in-a-haystack research names context rot: "as the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases" — "a performance gradient," not "a hard cliff."

The chapter's key point is that these "don't exist in isolation but form a system of competing trade-offs. When improving performance along one dimension, you typically create additional pressure on the others." It names three cyclic pressures:

• complexity → tools → context
• tools → context → complexity
• context → complexity → tools

The governing principle: "the smallest possible set of high-signal tokens that maximizes the likelihood of some desired outcome" — minimal-but-sufficient complexity decomposition, minimal-but-complete tool coverage, and minimal-but-adequate context retention.

This skill scores a configuration against that triangle. The scoring curves are transparent heuristics that embody the chapter's qualitative claims (exponential complexity load, ambiguity-dominated tool pressure, context-rot gradient); they are not chapter-cited benchmarks, and the production seam is documented at each lib function.