# run-agent

Published articles for run-agent.

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## Running Agent Harnesses with Local Models

DevFeed: [Running Agent Harnesses with Local Models](<https://devfeed.tech/articles/easiest-way-to-run-agent-harnesses-using-local-models-26896.md>)

Original publisher: [Read original article](<https://blog.dailydoseofds.com/p/easiest-way-to-run-agent-harnesses>)

Author: Avi Chawla

Published: 2026-09-15T21:59:31Z

Content type: tutorial

Language: en

Sources: [Daily Dose of Data Science](<https://devfeed.tech/sources/daily-dose-of-data-science.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [coding](<https://devfeed.tech/topics/coding.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [codex](<https://devfeed.tech/topics/codex.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [coding](<https://devfeed.tech/tags/coding.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [local](<https://devfeed.tech/tags/local.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [profiling](<https://devfeed.tech/tags/profiling.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [run-agent](<https://devfeed.tech/tags/run-agent.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

A video walkthrough explains how Magnitude profiles computer hardware, benchmarks local models, recommends practical candidates, and connects a selected model to coding agent harnesses such as Claude Code, Codex, OpenCode, and Pi. The article also presents a Dynatrace reference application for tracing LLM pipelines with OpenTelemetry.

### Source excerpt

...explained with a full video walkthrough.

## 130 Release-Roadmap Issues, 818 Agent Runs: A Mid-Iteration Reality Check

DevFeed: [130 Release-Roadmap Issues, 818 Agent Runs: A Mid-Iteration Reality Check](<https://devfeed.tech/articles/130-release-roadmap-issues-818-agent-runs-a-mid-iteration-reality-check-25419.md>)

Original publisher: [Read original article](<https://jonnyzzz.com/blog/2026/04/28/ai-agent-roadmap-research/>)

Author: Eugene Petrenko

Published: 2026-04-28T00:00:00Z

Content type: article

Language: en

Sources: [Eugene Petrenko](<https://devfeed.tech/sources/eugene-petrenko.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [codex](<https://devfeed.tech/topics/codex.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [issue tracker](<https://devfeed.tech/topics/issue-tracker.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Requirements](<https://devfeed.tech/topics/requirements.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [cli](<https://devfeed.tech/tags/cli.md>), [code](<https://devfeed.tech/tags/code.md>), [eugene-petrenko](<https://devfeed.tech/tags/eugene-petrenko.md>), [graph](<https://devfeed.tech/tags/graph.md>), [issue-tracker](<https://devfeed.tech/tags/issue-tracker.md>), [jonnyzzz](<https://devfeed.tech/tags/jonnyzzz.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-steroid](<https://devfeed.tech/tags/mcp-steroid.md>), [multi-agent](<https://devfeed.tech/tags/multi-agent.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [requirements](<https://devfeed.tech/tags/requirements.md>), [run-agent](<https://devfeed.tech/tags/run-agent.md>), [tag-41199d53f463](<https://devfeed.tech/tags/tag-41199d53f463.md>)

### AI overview

The article describes using a swarm of Claude, Codex, and Gemini agent CLIs to research 130+ release-roadmap issues and produce implementation specifications. The process used 818 agent runs over four days at an approximately $576 cost, followed by a mid-release comparison of the research with the team's actual work.

### Source excerpt

We turned one release's roadmap -- 130+ umbrella-epic issues -- into fully-researched implementation specs using a swarm of three different agent CLIs (Claude, Codex, Gemini): 818 runs, ~155M+ tokens, ~$576, four days. Then, mid-release-cycle, we ran a snapshot to compare the research against reality -- with no expectation that anything was "done" yet. Here is the pipeline, the numbers, and the honest verdict.

## Initial exploration of using Koog for developing Kotlin based AI agents

DevFeed: [Initial exploration of using Koog for developing Kotlin based AI agents](<https://devfeed.tech/articles/initial-exploration-of-using-koog-for-developing-kotlin-based-ai-agents-25198.md>)

Original publisher: [Read original article](<https://johnoreilly.dev/posts/kotlin-koog/>)

Published: 2025-06-21T23:00:00Z

Content type: tutorial

Language: en

Sources: [John O'Reilly](<https://devfeed.tech/sources/john-o-reilly.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Kotlin](<https://devfeed.tech/topics/kotlin.md>), [MCP](<https://devfeed.tech/topics/mcp.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Kotlin Multiplatform](<https://devfeed.tech/topics/kotlin-multiplatform.md>), [Android Studio](<https://devfeed.tech/topics/android-studio.md>), [Jetpack Compose](<https://devfeed.tech/topics/jetpack-compose.md>), [IntelliJ IDEA](<https://devfeed.tech/topics/intellij-idea.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [android-studio](<https://devfeed.tech/tags/android-studio.md>), [anthropic](<https://devfeed.tech/tags/anthropic.md>), [compose](<https://devfeed.tech/tags/compose.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [ide](<https://devfeed.tech/tags/ide.md>), [kmp](<https://devfeed.tech/tags/kmp.md>), [kotlin](<https://devfeed.tech/tags/kotlin.md>), [kotlin-multiplatform](<https://devfeed.tech/tags/kotlin-multiplatform.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-server](<https://devfeed.tech/tags/mcp-server.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [openai](<https://devfeed.tech/tags/openai.md>), [plugin](<https://devfeed.tech/tags/plugin.md>), [run-agent](<https://devfeed.tech/tags/run-agent.md>), [tool](<https://devfeed.tech/tags/tool.md>)

### AI overview

This article explores Koog, a Kotlin-based framework for building and running AI agents. It demonstrates an agent using Google Gemini, MCP servers, climate-emissions data, and Compose UI code added to an Android Studio project, and discusses support for multiple LLM providers and custom tools.

### Source excerpt

Koog, announced recently at KotlinConf, is a new Kotlin-based framework designed to build and run AI agents. This article will outline initial exploration of using Koog along with a number of configured MCP servers (specifically mcp-jetbrains which we'll use to control the IntelliJ/Android Studio IDE and also one based on the ClimateTraceKMP Kotlin Multiplatform (KMP) sample built using the Kotlin MCP SDK).