标签 · Claude Code — Glean

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43picks · chronological

07-15

How to Create Loops with Claude

This article advocates shifting from writing single prompts to designing loops—automated systems that keep AI agents working without human intervention. It breaks down a loop into six components: automation triggers, git worktrees for parallel isolation, skills (procedure manuals), connectors, sub-agents, and persistent memory files (e.g., STATE.md). The evaluator-optimizer pattern is highlighted: one agent generates, another verifies against objective gates like test suites or type checkers. Stop conditions must be checkable by external signals, not the agent's own claim. An autonomy ladder (suggest, draft, apply low-risk, full auto) helps gradually earn trust. The article also warns about token costs and the need for command allowlists in unattended loops.

x.com · 10 min · Agent Architecture · AI Engineering · Claude Code

07-14

Model and effort in Claude Code: knowing more vs. trying harder

This article by a Claude Code team member explains the real mechanism behind model and effort settings. Model selection swaps frozen weights (knowledge), while effort controls how much work Claude does—how many files it reads, tests it runs, and how thoroughly it verifies. Using analogies (specialist vs expert vs generalist) and diagrams, it clarifies when to upgrade the model (not enough knowledge) vs increase effort (not enough trying). Practical advice: start with default effort, choose larger models for hard problems, smaller ones for routine tasks to save cost. Key insight: check context first, then decide if Claude didn't know or didn't try hard enough.

x.com · 14 min · AI Engineering · Claude Code · LLM

07-14

The 'Caveman Skill' That Claims 65% Token Savings Actually Saves Only 8.5%

This article analyzes the recent trend of 'caveman skills' (such as the Caveman project) that prompt AI coding tools to output minimal language to save tokens. The author points out that the claimed 65% token savings comes from chat scenarios, whereas in agentic programming tasks, tool calls and system prompts dominate token usage. A controlled test by JetBrains (86 tasks, 240 trials) showed that even with forced activation, output token savings were only 8.5%, and in practice the savings are even smaller due to conditional activation. The article also discusses the cost of brevity: loss of information leads to more developer follow-ups and agent rework. The author argues that true cost optimization comes from context management (e.g., prompt caching) and reducing unnecessary tool calls, not from compressing output.

x.com · 2 min · AI Engineering · Claude Code · Prompt Engineering

07-12

Anthropic's Goodwill Drain: Lock-in, Price Hikes, and an Engineer's Reckoning

A firsthand account of Anthropic's deteriorating trust: unreliable APIs tied to subscriptions, a locked-in and buggy Claude Code ecosystem, and opaque billing changes that effectively raise costs. The author argues these moves fund model training, not product improvement. Their response: shift to an 'agent-assisted' workflow and swap Claude for open-source models like Qwen and GLM via OpenRouter, gaining cost control and data security. For engineers feeling trapped in a single AI platform.

raheeljunaid.com · 11 min · Agent Engineering · Anthropic · Claude Code

07-10

Getting started with loops in Claude Code

The Claude Code team defines four loop patterns (turn-based, goal-based, time-based, proactive) with trigger, stop criteria, use cases, and token management tips. Concrete commands like /goal, /loop, /schedule and a SKILL.md example show how to make agents iterate, self-verify, and compose primitives into automated workflows. A practical guide for developers exploring agent engineering.

x.com · 9 min · Agent Engineering · Claude Code · Context Engineering

07-10

Lessons from Building Claude Code: How We Use Skills

Anthropic engineers share practical lessons from maintaining hundreds of Claude Code skills internally. The post categorizes skills into nine types—library reference, product verification, data fetching, business process automation, code scaffolding, code quality, CI/CD, runbooks, and infrastructure ops—and offers concrete writing tips: focus on non-obvious hints, build gotchas sections, use the filesystem for progressive disclosure, avoid over-constraint, design setup flows, treat the description field as a trigger for the model, store memory (logs, JSON, SQLite), and bundle scripts/libraries to reduce token waste. It also covers distribution strategies (in-repo vs. marketplace), skill composition, and usage measurement via PreToolUse hooks. A must-read for anyone building agent-centric workflows.

x.com · 15 min · Agent Engineering · Agent Skills · Anthropic

07-08

Loop Patterns in Claude Code: A Practical Guide

The Claude Code team's official blog post introduces four loop modes: turn-based, goal-based, time-based, and proactive loops. It covers how each is triggered, stopped, and when to use them, along with token management and code quality tips. Practical CLI commands and SKILL.md examples are provided. The article emphasizes starting simple and gradually automating repetitive tasks. Essential reading for engineers using or evaluating Claude Code for autonomous development.

x.com · 9 min · Agents · AI Engineering · Ai Tooling

07-07

Agent harness engineering with Claude: 14-step roadmap from one agent to a self-improving system

This article presents a 14-step roadmap for building an Agent harness with Claude, from a single agent to a self-improving system. The author argues that the harness — comprising model, tools, permissions, and initial context — is the foundation for any loop, and a weak harness leads to slop. It covers concrete practices: file structure (.claude/), CLAUDE.md for standing facts, settings.json for permission pre-approvals, subagents for isolated contexts, skills for reusable procedures, hooks for deterministic enforcement, and memory for cross-session learning. The final steps add loops and dynamic workflows, closing the feedback loop where output → lessons → skills → better output. The article targets engineers who run or plan to run multi-agent code generation systems.

x.com · 16 min · Agent Architecture · Agent Engineering · Claude Code

07-07

Human-in-the-Loop Workflow Design: From Approval Fatigue to Planned Review

Based on an analysis of 400,000 Claude Code sessions, this article reveals that 93% of permission prompts are approved, leading to 'consent fatigue' where humans are nominally in the loop but functionally tuned out. The author proposes restructuring the workflow into three layers: input (precise task description, constraints, examples), steering (plan-level review instead of per-action approval), and output review (defining quality criteria and self-assessing). A single evaluation checkpoint improved generation quality by 8–10% in controlled tests. The article provides actionable steps to move from per-action approval to strategic intervention, targeting AI engineers and agent developers.

x.com · 11 min · Agent Architecture · AI Engineering · Claude Code

07-06

Better Models: Worse Tools

Pi author discovers that Anthropic's Opus 4.8 and Sonnet 5 inject spurious keys (requireUnique, oldText2, cost, etc.) into the edits[] array of Pi's edit tool, while older models do not. The failure is context-dependent and reproducible in agentic sessions. The post dissects Anthropic's tool calling internals: ANTLM markers, JSON-serialized nested arrays, and Claude Code's extremely forgiving harness that silently filters unknown keys and retries malformed calls. Author hypothesizes that RL post-training over Claude Code's flat old/new_string schema creates a strong prior, making newer models worse at following non-canonical tool schemas. Strict tool invocation fixes the issue, but Anthropic's complexity limits prevent Claude Code from using it. Key takeaway: tool schemas are not distribution-neutral; any harness must inherit Claude Code's quirks.

lucumr.pocoo.org · 14 min · Agent Engineering · AI · Claude Code

07-03

The Claude Opus 4.8 Setup Guide: How to Get Maximum Quality for Minimum Cost (Exact Config Inside)

A hands-on configuration guide published day after Claude Opus 4.8's release. The core value lies not in benchmark improvements (SWE-bench 87.6% → 88.6%) but in three operational features: Effort Control for per-task reasoning depth, Fast Mode at 3x cheaper than before, and Dynamic Workflows supporting up to 1,000 parallel subagents. The author provides a cost-optimization matrix routing tasks to Haiku/Sonnet/Opus at different effort levels, claiming ~50% monthly savings ($400-600 down to ~$205) for heavy users. Includes copy-paste configs for environment variables and settings.json. Practical for Claude Code users focused on cost control, though the savings claims are unverified estimates.

x.com · 9 min · Agents · Ai Tooling · Claude Code

07-02

Steering Claude Code: CLAUDE.md, skills, hooks, rules, subagents and more

This official guide from Claude Code maps out seven mechanisms for injecting instructions: CLAUDE.md, rules (with optional path scoping to save tokens), skills (dynamically loaded on invocation), subagents (fully isolated context, ideal for side tasks), hooks (deterministic triggers with low context cost), output styles (highest instruction weight, but replace defaults), and append-system-prompt (additive but has diminishing returns). It details when each loads, its context cost, and typical use cases. Key advice: use hooks for deterministic behavior over CLAUDE.md, skills for multi-step procedures, path-scoped rules for API-specific constraints, and managed settings for non-overridable guardrails. Aimed at engineers customizing Claude Code for production workflows.

claude.com · 11 min · Agent Architecture · Ai Tooling · Claude Code

07-01

Getting started with loops

This article is an official engineering guide from Claude Code that systematically lays out four agentic loop patterns and their use cases. Turn-based loops are for short exploratory tasks; users encode manual verification steps into SKILL.md — e.g., asking Claude to start a dev server, take screenshots, and check the browser console. Goal-based loops, triggered by /goal, define deterministic termination criteria such as 'get the Lighthouse score to 90 or above' and force iteration until the target is met. Time-based loops come in two flavors: /loop for local polling on an interval and /schedule for cloud-triggered routines, ideal for recurring work like PR review or CI fixups. Proactive loops combine /schedule, /goal, dynamic workflows, and auto mode into a pipeline for long-running, well-defined streams of work. The article also covers code quality maintenance and token usage management: encoding conventions, using scripts instead of re-reasoning, routing routine work to cheaper models, and monitoring cost with /usage. Suitable for engineers embedding Claude Code into daily dev workflows.

claude.com · 8 min · Agent Engineering · Claude Code · Context Engineering

06-30

5 Agent Skills I Use Every Day to Encode My Development Process

Matt Pocock, a seasoned engineer, shares 5 agent skills he uses daily to encode rigorous, repeatable processes for LLM agents, addressing their lack of memory and tendency to drift. Key skills include: grill-me (exhaustive questioning before coding), to-prd (turning discussions into PRDs), to-issues (slicing PRDs into vertical issues), tdd (forcing red-green-refactor cycles for quality), and improve-codebase-architecture (identifying shallow modules for deepening). The core insight: short, well-crafted skills can dramatically boost agent output quality.

www.aihero.dev · 8 min · Agent Skills · Claude Code · Process Encoding

06-28

The 5 Levels of Loop Design: From Prompting to Autonomous Agents

The creator of Claude Code says he no longer writes prompts—loops prompt it instead. This post introduces a 5-level progression of human-AI workflow: from Level 1 (single-turn prompting), through Level 2 (manual loop of do-check-correct), Level 3 (verified loop with separate judges for 'done'), Level 4 (self-running loop using /goal command with guardrails), to Level 5 (autonomous systems where loops self-start, run in parallel, and persist lessons into a skill base). Each level comes with a tell and a concrete next step. For developers who still feel they are 'babysitting' their AI agents.

x.com · 7 min · Agent Architecture · Agent Engineering · Agents

06-28

Loop Engineering: When Prompting Takes a Back Seat to the System

This article, inspired by Claude Code creator Boris Cherny, argues that the center of gravity in agent development has shifted from prompt engineering to loop engineering. It unpacks the trivial core loop and identifies four hard problems: knowing when to stop (distinguishing tool-call cessation from task completion), maintaining context hygiene to avoid decay, designing tools that agents can actually use (idempotent writes, error messages for LLMs), and embedding a critic in the loop to prevent self-agreement. The piece underscores that the model is commoditized; the loop—the harness—is where real engineering value lies. A must-read for engineers building autonomous agent systems.

x.com · 8 min · Agent Architecture · Agent Engineering · Claude Code

06-27

Stop Being the Loop: How to Make Claude Work While You Sleep

Boris Cherny, who built Claude Code at Anthropic, no longer writes prompts by hand—he writes loops. This guide explains what a real loop is: a small system that runs Claude repeatedly until a job is done, complete with self-checking, state persistence, and automatic stopping. Unlike cron jobs, loops contain a decision-maker (Claude) that can adapt mid-stream. The article covers Claude Code's /goal (loop until done) and /loop (repeat on a schedule) commands, and provides a paste-ready charter template with sections for goals, work sources, work instructions, self-verification, memory, and stop conditions. Ideal for engineers transitioning from prompting to building persistent, autonomous AI workflows.

x.com · 9 min · Agent Engineering · Agent Orchestration · AI Engineering

06-26

ECC: Cross-Harness Agent Operating System for Claude Code, Cursor & Beyond

ECC is a comprehensive agent harness operating system designed to enhance multiple AI coding assistants including Claude Code, Cursor, Codex, OpenCode, and GitHub Copilot. It's not a standalone AI tool but a collection of 260+ skills, 67+ agents, persistent memory, continuous learning, cost optimization, and security auditing features. By providing unified rules, hooks, and MCP configurations across platforms, it addresses issues of inconsistent agent behavior, context loss, and inadequate security. Ideal for professional developers deeply using AI coding assistants and teams seeking standardized agent engineering practices.

github.com · 94 min · Agent Engineering · Claude Code · Context Engineering

06-26

Loop engineering: the 14-step roadmap from prompter to loop designer

This post from @0xCodez on X provides a comprehensive 14-step roadmap for transitioning from manual prompting to designing autonomous loop systems in AI-assisted coding. Based on Anthropic engineering docs, Addy Osmani's essay, and recent studies, it's structured in three tiers: first, a 4-condition test to decide if a loop is warranted; second, five building blocks (automations, worktrees, skills, connectors via MCP, sub-agents with maker-checker split); third, building the minimal viable loop and avoiding failure modes like the 'Ralph Wiggum loop', comprehension debt, and security tax. The author emphasizes that loops are not universal—they only earn their cost when tasks repeat, verification is automated, the token budget can absorb waste, and the agent has senior engineer tools. Ideal for engineers already using coding agents who want to orchestrate them into batched, automated workflows.

x.com · 23 min · Agents · AI Engineering · Ai Tooling

06-25

How 1 Claude Agent Runs 10 Others · 9 Steps Swarm Loop

This post breaks down a 9-step pattern for running multiple Claude Code subagents in parallel without chaos. The key insight is that launching many agents is easy and usually messy; the skill is in the orchestration loop: verify the task is splittable, have the lead agent decompose it into atomic subtasks with human approval, isolate each agent via Git worktrees to prevent file collisions, then fan out in parallel. A SubagentStop hook gates every result (tests + lint), a grader scores each against a rubric and automatically sends weak ones back for revision, and finally only the lead agent merges passing results in dependency order. The entire loop can be packaged as a reusable skill or slash command.

x.com · 10 min · Agent Architecture · Agent Engineering · Claude Code

06-24

Loop Engineering: How One Loop Ships 259 PRs a Month

This article breaks down the engineering of AI-driven development loops, contrasting a single engineer shipping 259 PRs in a month with a runaway loop that burned $47,000. It dissects six essential components—state file, automation/scheduling commands (e.g., /loop, /schedule, /goal), git worktrees, skills, MCP connectors, and sub-agents (writer vs. checker)—with concrete configuration examples for both Claude Code and OpenAI Codex. The piece provides a brake configuration template (max_turns, max_budget_usd, scope, circuit_breaker), describes four failure modes, and offers low-cost starting strategies. Aimed at engineers building or evaluating AI agent workflows.

x.com · 12 min · Agent Engineering · Ai Tooling · Claude Code

06-23

How To Use Loop Engineering To Build A Self-Improving Quant Trading System

Written by a backend developer working on quant trading systems, this article argues for moving beyond manual prompt-and-wait workflows to building self-running loops. It dissects six universal components of production loops: automation hooks, skill files (SKILL.md), state files (STATE.md), a separate verifier agent, Git worktrees for isolation, and MCP-based connectors. The author then wires these around the five-stage quant trading cycle (data ingestion, signal generation, verification, execution, risk monitoring), with a feedback mechanism that writes lessons back into the skill file for continuous improvement. Targeted at engineers building AI-driven or automated systems, especially in finance.

x.com · 13 min · Agents · Claude Code · Loop Engineering

06-23

From Prompting Agents to Loop Engineering

The AI coding community is shifting from prompting agents manually to designing loops that prompt agents for you. This is the most practical, production-oriented guide available: what an agent loop is, why it matters, and what one looks like in the real world. The author breaks down the six mandatory components (trigger, isolation, written-down context, tool reach, second-agent checker, on-disk state), then illustrates with two concrete examples: a PR babysitter that checks every 15 minutes and auto-fixes CI failures, and Claude Code's /goal command. It also covers where cost actually goes (iterations, not tokens), when not to loop (one-shot edits, unbounded exploration), and predictable failure modes (verification burden stays human, comprehension debt, silent drift).

x.com · 15 min · Agent Engineering · Agent Loop · CI/CD

06-23

Loop Engineering

Loop Engineering proposes a shift from hand-prompting coding agents to designing autonomous loops: a system with five components (scheduled automations for discovery, worktrees for parallel isolation, skills to codify project context, plugins/connectors via MCP, and verifier sub-agents) that lets agents iterate without manual intervention. The post maps these primitives across Codex and Claude Code, noting that memory persisted outside the conversation (via AGENTS.md or Linear) is the critical sixth piece. The core insight is that loop design is harder than prompt engineering—the engineer's role moves from operator to system architect, while verification burden, comprehension debt, and cognitive surrender remain unresolved challenges that the loop itself cannot eliminate.

addyosmani.com · 14 min · Agent Architecture · Agent-Memory · Ai Tooling

06-22

The Debug Loop: How Claude Code Finds the Bug in 6 Steps Instead of 60

Most developers debug with Claude Code by pasting errors and accepting speculative fixes, leading to a 40-60 message death spiral. This post proposes a six-step loop: first establish a reliable repro (failing test), isolate the search area in plan mode, dispatch read-only subagents to trace root causes from multiple angles, fix only the root cause (not symptoms), verify with an automatic hook (e.g., PostToolUse running the test), and keep the repro as a permanent regression test. The key insight is that Claude Code was always capable; the failure mode is skipping straight to 'fix' before understanding the bug.

x.com · 7 min · Agent Engineering · Claude Code · Debugging

06-22

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide)

A step-by-step guide to building a persistent 'second brain' using Claude and Obsidian, based on Andrej Karpathy's LLM Wiki pattern. Obsidian stores all notes as local plain text files, while Claude (via MCP protocol) reads, organizes, and links the entire vault. Key steps: install Claude Desktop (paid plan), install Obsidian with Local REST API plugin, connect via MCP, create a CLAUDE.md profile via interview, structure projects with Inputs/Process/Outputs/Feedback folders, build reusable skills, wire in live data (calendar, email), and set up autopilot scheduling. The author stresses ownership (plain text, vendor-independent) and security ('keys, not prompts'). Aimed at engineers and knowledge workers tired of context loss.

x.com · 11 min · Ai-Memory · Claude Code · Context Engineering

06-21

Loops explained: Claude, GPT, Mira and what actually works

This article explains the core concept of AI loops—moving from single-request interactions to autonomous goal-driven iterations. It covers the five building blocks of a real loop (automation, skill, sub-agents, connectors, verifier), the hidden costs (context growth, token compounding, empty iterations), and provides a self-checking prompt you can paste into any LLM. It also introduces Mira as a free, no-code loop platform inside Telegram. For engineers evaluating agent infrastructure or anyone wanting to move beyond manual prompting.

x.com · 19 min · Agent Architecture · Claude Code · AI Loops

06-20

Thin Harness, Fat Skills

YC partner Garry Tan argues that the bottleneck in AI agents is not model intelligence but context and process management. He introduces five definitions: Skill files (reusable Markdown procedures), a thin harness (a ~200-line loop for running the model and managing context), resolvers (routing tables that load the right context at the right time), the latent-versus-deterministic boundary (judgment vs. repeatable execution), and diarization (distilling structured intelligence from unstructured data). A real-world example from YC Startup School demonstrates how the same skill file, invoked with different parameters, handles breakout grouping, lunch matching, and real-time pairing, and then improves itself by analyzing mediocre feedback. The piece offers concrete design principles for engineers building agent systems that compound improvements over time.

x.com · 12 min · Agent Architecture · Agents · Ai Tooling

06-19

On the LOC controversy: doing the math on a 810x developer output increase

Garry Tan, CEO of Y Combinator, responds to criticism of his claim of shipping 600,000 lines of production code in 60 days. He concedes LOC is a flawed metric but provides a rigorous before-and-after comparison: in 2013, as a part-time coder, he averaged 14 logical lines per day; in 2026, with the same day job, he now averages 11,417. Even after deflation for logical SLOC and an aggressive 2x AI-verbosity factor, the daily rate is 5,708 lines – a 408x increase. Quality data is provided: 2.0% revert rate, 6.3% fix commits, and a test suite that grew from 100 to over 2,000 tests. He details his testing infrastructure (Playwright-based browser CLI, slop-scan), product traction (75k GitHub stars, ~7k WAU), and argues the real shift is the collapse of the “idea to shipping" cycle from weeks to hours. The core argument is that the productivity ground has shifted for all engineers, not just him.

x.com · 12 min · AI · Ai Tooling · Claude Code

06-19

Resolvers: The Routing Table for Intelligence

Garry Tan argues that resolvers—lightweight context routers—are the missing governance layer in agent systems, more crucial than models or skills. Using a mis-filed article as a trigger, he demonstrates how a 200-line resolver replaced 20,000 lines of crammed context, fixing model attention degradation and knowledge base drift. He details a production audit revealing that 10 of 13 skills ignored the resolver, and how he built trigger evals, a "check-resolvable" meta-skill to detect dark capabilities, and a self-healing loop against context rot. The piece reframes resolvers as the organizational chart and management of an agent system, and announces the open-sourcing of his personal architecture (GBrain/GStack) that embodies these patterns. Key evidence: real agent managing 25,000 files and 200 daily inputs, with concrete metrics on skill reachability defects.

x.com · 18 min · Agent Architecture · Agents · Ai-Memory

06-18

A Structured Cybersecurity Skills Library Purpose-Built for AI Agents

This is not another collection of security scripts or checklists. It’s an AI-native knowledge base that encodes 754 practitioner-grade cybersecurity workflows into a structured, agent-readable format. Each skill carries YAML frontmatter for sub-second discovery and step-by-step Markdown procedures, essentially giving any LLM-based agent the decision-making playbook of a senior analyst. The library spans 26 domains—from DFIR and threat hunting to cloud security and OT/ICS—and maps every skill to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF, making it uniquely suited for security professionals integrating AI into real operational workflows.

github.com · 28 min · AI Agents · Claude Code · Cybersecurity

06-17

Agentic coding and persistent returns to expertise

Anthropic analyzed ~400,000 Claude Code sessions, finding that users make most planning decisions while Claude handles execution. Domain expertise, not coding background, is the key to success: expert-rated sessions achieve verified success over twice as often as novices, though intermediate users capture most of the benefit. Non-software occupations succeed at coding tasks within 5 points of software engineers. Over seven months, the share of debugging sessions fell from 33% to 19%, while end-to-end tasks like deployment, data analysis, and document writing grew, and estimated task value rose ~25%. The report details methodology for decision attribution, expertise classification, and success verification, along with limitations. Suitable for engineers and researchers interested in AI coding tools, agent collaboration, and skill transfer.

www.anthropic.com · 27 min · Agents · AI Engineering · Claude Code

06-17

Kimi Code + K2.7 Code Hands-On: Can It Replace Claude Code?

A hands-on evaluation of Kimi Code paired with the K2.7 Code model as a potential Claude Code replacement. Tests include using video understanding to replicate an ink-wash animation, using the /goal command to autonomously compress a 2.1MB image to below 120KB, and running a suite of web UI, game, and animation programming challenges. Kimi Code is found to be highly compatible with Claude Code's commands and permission system. The /goal command enables fully unattended task execution. The K2.7 model demonstrates stable code generation capability with a claimed 30% average reduction in reasoning token consumption. A unique built-in Datasource plugin allows querying real-time financial data, company records, and academic papers via natural language within the CLI.

mp.weixin.qq.com · 1 min · Agent Architecture · Ai Tooling · Claude Code

06-15

Claude Official Cookbooks: Engineering Recipes from RAG to Multimodal Agents

Anthropic's official collection of practical coding recipes for building with Claude. It provides runnable Jupyter notebooks covering capabilities like classification, summarization, and RAG, alongside advanced techniques such as tool use, multimodal vision, and sub-agent orchestration. The latest additions introduce the Claude Agent SDK and Managed Agents, demonstrating how to build observable, hostable agents—from research assistants to SRE bots—with just a few lines of code.

github.com · 8 min · Agents · AI Engineering · Anthropic

06-13

Build a Self-Improving Agent System with Claude Fable 5 in 14 Steps

A practical guide based on Anthropic engineering posts and experiments detailing how to build a self-improving agent system around Claude Fable 5. It argues most users underutilize the Mythos-class model, treating it like a bigger Sonnet 4.6. The architecture layers primitives (model, sub-agents, worktrees), orchestration (/goal and Outcomes loops, Dynamic Workflows, Routines for cloud execution), memory (state files and compounding Skills), and self-improvement (vision self-checks, eval loops, rule distillation). Key tactics include using an independent verifier sub-agent instead of self-critique, ensuring parallel safety with git worktrees, running multi-day tasks on cloud infrastructure, and following a 5-stage memory progression from failure documentation to general rule consultation. Designed for engineers building compound systems rather than prompting for minutes.

x.com · 28 min · Agent Architecture · AI Engineering · Claude Code

06-12

How an Anthropic seller rebuilt his team's workflows with Claude Code

Jared Sires, a former account executive at Anthropic with no coding experience, used Claude Code to build CLAFTS, a Gmail-integrated tool that drafts customer emails in his voice while pulling context from live product documentation. The tool saves 10-15 hours per week. He expanded this into a sales plugin with skills for daily briefs, recaps, and pipeline management, wired into Salesforce, Gong, and other systems via MCP servers. About 80% of Anthropic's sales org now uses the plugin. The piece illustrates how non-technical practitioners can leverage AI coding tools to eliminate technical barriers and deliver workflow-specific software.

claude.com · 9 min · Agent Architecture · AI Engineering · Claude Code

06-12

25 Claude Features, Workflows, and Tricks That Most Users Don't Know

A practical guide by @eng_khairallah1 detailing 25 workflows and techniques to fully leverage Claude Projects. The core thesis is treating Projects as evolving, persistent workspaces rather than transient chat sessions. It provides actionable strategies including a structured instruction template, strategic file organization, the Living Instructions pattern, and advanced concepts like voice calibration files and competitive intelligence hubs. The guide emphasizes a compounding knowledge strategy where each interaction refines Claude's contextual understanding, suitable for power users aiming to transform Claude from a generic tool into a domain-specific specialist.

x.com · 16 min · Agent Architecture · AI Engineering · Anthropic

06-09

Loop Engineering: Designing the System That Prompts Your Coding Agents

Addy Osmani argues that interacting with coding agents is shifting from prompt engineering to 'loop engineering'—designing a system that autonomously discovers tasks, delegates work, and verifies results using five building blocks: scheduled automations, parallel worktrees, project skills, connector plugins, and checker sub-agents. He maps how Claude Code and Codex both implement all five, noting that the leverage point has moved from writing good prompts to architecting persistent loops. The post cautions that loops amplify existing problems: verification, comprehension debt, and cognitive surrender become sharper risks. Intended for senior engineers evaluating how to productize AI coding tools beyond one-shot interactions.

x.com · 14 min · Agent Architecture · AI Agents · AI Engineering

06-09

AI Agents: What They Are and How to Build a Telegram Bot with Claude Code

This guide clarifies that AI agents are not a category but a spectrum from simple chat to autonomous loops, defined by tools, memory, and a loop. It then provides a no-code, step-by-step tutorial on building a Telegram bot agent with Claude Code, including system prompt templates, systemd deployment, persistent memory, cost tracking, and practical skills. Also addresses the common memory problem and offers concrete fixes. Suitable for engineers who want a practical agent without writing code themselves.

x.com · 17 min · Agents · Ai Tooling · Claude Code

06-08

Composable Agent Skills for Real Engineering Workflows

Matt Pocock's personal agent skills for Claude Code and Codex, targeting four common failure modes in AI-assisted development: misalignment, verbosity, broken code, and design entropy. Instead of controlling the process, these small, composable skills embed engineering fundamentals—grilling sessions for alignment, shared ubiquous language for concision, TDD red-green-refactor loops for code quality, and architecture rescue tools. They work with any model and are designed to be hacked and adapted in your own .claude directory.

github.com · 14 min · Agents · AI Engineering · Claude Code

06-08

Every Agentic Engineering Hack I Know (June 2026)

The author shares 22 practical hacks for agentic engineering with Claude Code and Codex. The core is a plan-first workflow: use /ce-plan to generate a plan.md that guides the agent; humans skim or ask inline instead of reading it. Hacks include: voice input via Monologue or Wispr Flow (LLMs handle imperfect transcription); running 4-6 separate agent sessions in cmux tabs; defaulting terminal tabs to Claude Code and bypassing all permission prompts with sound alerts on completion; giving Claude an email address via AgentMail to trigger sessions remotely; using last30days before planning to search community discussions and news in parallel; turning repeated tasks into reusable skills to compound agent capabilities. He stresses that human value lies in providing taste and direction, not typing, and warns against AI addiction. The post is packed with copy-paste config snippets and concrete tools, aimed at engineers deep into AI-assisted development.

x.com · 28 min · Agent Infrastructure · Agents · AI Engineering

06-08

How to Master Dynamic Workflows in Claude Code: 6 Patterns and 14 Steps

This article provides a systematic guide to Dynamic Workflows in Claude Code, shipped in late May 2026. It moves beyond manual prompt chaining by letting Claude generate a bespoke JavaScript harness for a specific task. The author first explains the mental model: how workflows structurally fix agentic laziness, self-preferential bias, and goal drift inherent in single-context sessions. It then breaks down six core patterns—classify-and-act, fan-out-and-synthesize, adversarial verification, generate-and-filter, tournament, and loop until done—each with code skeletons. Real-world use cases show how to compose patterns for migrations, deep research, triage, and lightweight evals. Practical controls like /goal, /loop, token budgets, and the quarantine pattern for untrusted inputs are covered. It also advises on saving successful workflows and shipping them as Skills. This guide is for engineers aiming to tackle long-running, parallel, or adversarial tasks beyond a single Claude Code session.

x.com · 17 min · Agents · AI Engineering · Anthropic

06-04

A harness for every task: dynamic workflows in Claude Code

Anthropic engineer Thariq Shihipar details dynamic workflows in Claude Code, where Claude auto-generates custom JavaScript harnesses to orchestrate multiple subagents. It explains how this overcomes single-context-window failures like agentic laziness, self-preferential bias, and goal drift. Common patterns such as classify-and-act, fan-out-and-synthesize, adversarial verification, and tournament are illustrated with concrete use cases from migrations and deep research to root-cause analysis. The post candidly advises that workflows are token-heavy and not needed for routine coding, offering practical tips on token budgets, saving workflows as skills, and pairing with /goal and /loop.

x.com · 15 min · Agent Architecture · Agents · Ai Tooling