agents
12 notes tagged agents in the AI agents topic. How agents are built, tuned and run: loops, harnesses, MCP, skills and coding agents, with what broke and what we measured.
- Agentic engineering: the series map and how to read it
A series on building agentic software for engineers who know the classic lifecycle, and how its pillars, deep dives and reading paths fit together.
- Owner agents, plain functions: where the model sits in our ops estate
Give each domain one owner agent that reports back, keep the steps as plain functions, and add autonomy to a running platform one gated decision at a time.
- 3,016 delegations to agents: the long briefs cost the most
Median brief 289 words, median run 6.4 minutes, 14.5% needed a follow-up. What 24 days of Claude Code subagent logs say about briefing agents, and where a brief cost…
- Harness engineering notes from a team running agents all day
Nine field notes on running coding agents in production at Dwarves: the harness around the model, how we check and guard it, what we put it to work on, and what it does…
- Five layers to tune before you fork your agent
What makes AI agentic, how to split a deterministic core from an agentic edge, and the five layers you can tune without rebuilding the harness. Real numbers from circle…
- Claude Code 2.1.259 turned our Read() deny rules into a Bash tripwire
Bypass-mode sessions started stalling on 'a Read() deny rule is configured' prompts. What the harness now infers from a Bash command, why a CLAUDE.md rule couldn't stop…
- The debug loop: make the agent measure the browser before it touches the CSS
A repeatable loop for AI-driven UI debugging: name the metric, name the axis, drive a real browser over CDP, read the DOM into a table, kill theories, sweep the suspect,…
- DeepSeek harness, dissected
An architecture read of dsh, DeepSeek's new agent harness: the plugin tree, the patch-layer boot, the log-first session model, and what it means for a Claude Code shop.
- AI agent explained: LLMs, workflows and agents
Most people confuse LLMs, workflows, and agents, but understanding these three levels unlocks the real potential of AI automation. The key difference lies in who makes…
- Building chatbot agent to streamline project management
A technical case study detailing the implementation of an AI chatbot agent in a project management platform. Learn how the team leveraged LangChain, LangGraph, and GPT-4…
- Building data pipeline for OGIF transcriber
A technical case study of creating an automated system that downloads videos, processes audio, and generates transcripts using AI services like Groq and OpenAI.
- Project reports system: a case study
An in-depth look at Dwarves' monthly Project Reports system - a lean, efficient system that transforms communication data into actionable intelligence for Operations…