Agent steering
How prompts, context, tools, and engineering harnesses shape the output produced by coding agents.
Agent output depends on the work definition, available context, tools, guardrails, and feedback loop. The articles here treat steering as an engineering practice that can be inspected and improved. They focus on patterns tied to reviewable outcomes, rather than rating people by prompt length or raw session volume.
Questions this topic helps answer
- Which parts of a coding-agent run most affect the resulting pull request?
- How can a team learn from successful runs without forcing one rigid workflow?
- What evidence distinguishes a useful steering pattern from extra activity?
Each article identifies its author, publication and update dates, related reading, and the Tetriz capability connected to the subject. Quantitative claims should state their source and limits under the research methodology.
Articles about agent steering
The four categories of enterprise AI and why engineering needs its own
Enterprise AI categories split into four types, and most purchasing conversations conflate all of them. A CTO who cannot tell these apart will spend budget on a tool that delivers seat usage dashboards when what the board actually needs is session-level attribution.
Prompt-to-PR attribution: what it is, why GitHub cannot track it, and how to evaluate it
Prompt-to-PR attribution connects the engineer's AI coding session to the pull request it produced. It links AI input quality, agent-authored code percentage, and review outcome in a single dataset. No engineering intelligence tool instruments at this layer today.