# comparative-studies

Published articles for comparative-studies.

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## Measuring how coding-agent context delivery affects results per token

DevFeed: [Measuring how coding-agent context delivery affects results per token](<https://devfeed.tech/articles/does-it-matter-how-you-feed-an-agent-its-context-20384.md>)

Original publisher: [Read original article](<https://tech.olx.com/does-it-matter-how-you-feed-an-agent-its-context-ee1519586520?source=rss----761b019b483f---4>)

Author: Raymond Gitonga

Published: 2026-08-21T13:36:56Z

Content type: opinion

Language: en

Sources: [OLX](<https://devfeed.tech/sources/olx.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [coding](<https://devfeed.tech/topics/coding.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Testing](<https://devfeed.tech/topics/testing.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-experiments](<https://devfeed.tech/tags/ai-experiments.md>), [backend](<https://devfeed.tech/tags/backend.md>), [coding](<https://devfeed.tech/tags/coding.md>), [comparative-studies](<https://devfeed.tech/tags/comparative-studies.md>), [context](<https://devfeed.tech/tags/context.md>), [cost](<https://devfeed.tech/tags/cost.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [testing](<https://devfeed.tech/tags/testing.md>)

### AI overview

The article describes an experiment comparing four ways of delivering context to a coding agent. The same task, model, scoring method, and reference files were used across three runs of each setup, with the goal of measuring results relative to token cost. The task was adding a notification worker to a mature backend system spanning three codebases and approximately 18-20 files.

### Source excerpt

I gave a coding agent the same job four different ways and measured what each cost. The packaging barely mattered, but something else did. At OLX, we've been pushing to get AI tools into everyday engineering work. This has enabled us to have faster delivery and better test coverage, with less time spent on the mechanical parts. Our biggest constraint, however, is tokens. Tokens being a finite resource, we keep running up against our allocated quota. So the real question is how to get the best results per token spent. There's no shortage of advice on how to feed context into agents: put everything into one prompt, split it into folders, or give one orchestrator the whole picture and let it delegate. But opinions vary on which way is better and which produces the best results relative to cost. So I decided to measure it. One job, four setups, three runs each: same model, same task, same scoring. The only thing that changed was how the agents got their context. The Setup The job was to add a notification worker to one of our fairly mature backend services. In plain terms, the worker watches for things happening in the system, decides who should get a notification, and logs the outcome. The worker lives in one service, but the change touched around 18-20 files across three codebases that talk to each other, tests included. All three have years of history and firm architectural conventions. In short: a realistic change in a mature system, not a toy task. Every setup got the same reference files, written once, and mostly derived from each codebase's own AGENTS.md file. They cover a repo map, one deep dive per codebase, the coding and testing conventions, and the contracts between the services. The CLAUDE.md files you'll see in the setups below are different: those are each setup's entry point, and part of what varied. The reference files were identical in every setup. Only the delivery changed. 1. Default: One manager owns everything. It keeps context in memory and spins