#coding-agent

ACP standardizes the boundary between coding-agent clients and coding-agent implementations: clients own the user interface and environment, agents own the model loop, and both…

AGENTS.md should be treated as scoped, repo-owned operating instructions: useful enough to load automatically, but never powerful enough to override user instructions, tool…

OpenAI's Codex guide argues that Codex performance depends as much on harness prompt/tool design as on model choice: prompts should emphasize autonomy, efficient exploration…

Public coding-agent prompts converge on layered prompt assembly: base identity, behavior policy, tool/editing constraints, environment and project context, then the user's…

There is no single standard coding-agent session format. Mature agents converge on a few concepts that survive specific implementations: durable append-only records, explicit…

A strong context-control system treats model context as an inspectable, user-controlled working set assembled from durable session state, scoped project instructions, explicit…

Context observability explains how a model request was assembled and how that request behaved. It can cover candidate context, selected context, transformations, provider…

Goose keeps its prompt surface as small markdown templates for agent identity, planning, compaction, subagents, recipes, app generation, permissions, and tiny-model behavior.

OpenAI's harness engineering essay argues that agent-first software development shifts engineering effort away from hand-writing code and toward designing the repositories…

Pi argues that a coding harness can stay small and inspectable by exposing simple tools, explicit context, event-streamed agent state, file-based planning, and terminal-native…

Pi's .pi/prompts directory is a compact set of procedural slash-command prompts for maintainer workflows, written as explicit checklists with safety rules and expected output.

Notes on Polytoken's local-first coding-agent daemon, sessions, facets, prompt input, work management, and permissions.

Release scope for a terminal coding harness should be defined by user-visible contracts: provider reliability, sessions, configuration, file-change safety, packaging, docs, and…

SafeExec is a Bash-based command interception layer for local human and AI-agent shell sessions: it gates a small set of destructive command shapes with a real TTY confirmation…

Tau names two related coding-agent efforts: dpc's Tau emphasizes Unix-native process composition, while Hugging Face Tau is a small Python teaching harness whose core lesson is…

Modern terminal coding-agent UIs converge on a chat transcript, bottom prompt, visible execution state, commands, review loops, and temporary detail surfaces; the main design…

Token optimization in tool-using agents covers several distinct problems: reducing verbose inputs, limiting fixed prompt overhead, removing context that has outlived its…