标签 · Agent Architecture — Glean

Recent picks

40picks · 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

07-10

Continual Learning for Agents: Eval, A/B, and Self-Improvement at Replit

This article argues that continual learning for agents isn't limited to weight updates—agents using closed frontier models can improve via harness and context layers. Using Replit Agent as a case study, it details a three-layer evaluation system: ViBench, an offline benchmark for vibe coding that scores apps built from scratch against natural-language PRDs; online A/B tests to capture real user behavior; and Telescope, a trace analysis system that clusters failure patterns. These feed a self-improvement loop that automatically proposes patches (reviewed by engineers). A concrete example shows how a cold-start regression was detected, fixed, and shipped in one day. The piece is relevant for engineers building AI agents and evaluation infrastructure.

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

07-08

Harness Engineering for Self-Improvement

This comprehensive survey by Lilian Weng systematically examines the critical role of harness engineering in recursive self-improvement (RSI) for AI systems. A harness is the system layer surrounding a base model that orchestrates execution, context management, tool calling, persistent state, and workflow design. The post synthesizes three design patterns (workflow automation, filesystem as persistent memory, sub-agents and backend jobs) and dives into frontier works: context engineering (ACE, MCE), meta-optimization (Meta-Harness), workflow automation search (ADAS, AFlow), self-improving harnesses (STOP, Self-Harness), and evolutionary search (AlphaEvolve, Darwin Gödel Machine). It concludes with open challenges: weak evaluators, memory management, diversity collapse, reward hacking. Essential reading for AI engineers and agent researchers.

lilianweng.github.io · 42 min · Agent Architecture · Agents · AI Engineering

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

07-05

Loop Engineering: What Comes After the Loop

This article argues that naive agent loops relying on LLMs for autonomous decision-making have hit four fundamental walls: plans that rot in the context window, unbounded retries (no distinction between hard and impossible), self-written tests that pass wrong answers (maker-votes-checker), and actions that cannot be replayed. The author demonstrates with runnable code: the same model in a naive loop returns 747 (ground truth 615) with SUCCESS, while a graph harness with a verifier gate returns exactly 615, rejecting 12 corrupted outputs and absorbing 51 tool errors with fewer calls. Four properties fill these walls: explicit dependencies (plan as static graph, not remembered text), bounded recovery (fixed staircase: retry→patch→replan), immutable append-only log (hash-chained, replayable for audit), and a verifier gate (output must pass an independent check node, maker cannot talk past). The twist: frontier frameworks now have the model itself draw the graph of what to do (Anthropic dynamic workflows), while durable state, logging, and verification are infrastructure, not reasoning, and are used even by minimalists. The honest brake: most loops today don't need a graph yet—pick the one wall that cost you this week and close it with a single property.

x.com · 18 min · Agent Architecture · Graph Harness · Loop Engineering

07-04

Superpowers: How to Make an AI Agent Run All Night and Deliver Usable Results

The author shares their journey from a failed attempt at orchestrating long-running AI agent tasks to discovering the Superpowers Skill Set, which solves the core pain points. Superpowers decomposes the development workflow into three phases: brainstorming, writing-plans, and executing-plans (with subagent-driven-development). Key design elements include: using separate prompt templates (implementer, spec-reviewer, code-quality-reviewer) to enforce separation of concerns; spinning up a fresh subagent for each task to avoid cascading context pollution; using hard constraints like "Never/HARD-GATE" to prevent agent deviation; and enforcing software engineering best practices such as TDD, DRY, and YAGNI. The author argues that with frontier models like Opus 4.8 and Codex GPT-5.5 now being sufficiently capable, the real bottleneck is harness design—using clear specifications and structured processes to make even cheaper models reliable for long-duration tasks.

justinyan.me · 4 min · Agent Architecture · Agent Engineering · Context Engineering

07-02

Building a Good Vertical Agent: Context as a Cache Hierarchy

The article argues that a good vertical agent is a faithful compression of its task distribution, and its context should be organized as L1/L2/L3 cache tiers. Using their Shortcut spreadsheet agent as example, they detail extreme optimizations: reading a range compresses 500 formulas into a single legend line via R1C1 normalization and aliasing; after writing, a structured diff groups, samples, and triages changes, flagging #REF! errors under MUST FIX. L2 provides curated English specs fetched on demand, like the pivot table recipe that bakes in gotchas (suspendLayout/resumeLayout, raw integer 8 for aggregation). L3 is the raw API reference plus a 100-line grep skill that lets the model mine tens of thousands of lines in bounded steps. The prompt budget mirrors the frequency curve, and the hierarchy moves as models improve. Practical, transferable advice for engineers building reliable agents in any domain.

x.com · 21 min · Agent Architecture · Agent Infrastructure · AI Engineering

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

What I Learned About Agent Skills from Building Popular Ones

The author, having built several popular Skills (PPT, social media cards, logo generator, AI desk card), argues that Agents amplify rather than erase capability gaps. A Skill is defined as a reusable capability unit that bundles expert experience, workflows, taste, and tool calls. Core insights: Skill design is externalizing human taste as constraints (e.g., no pure white/black, text must not cover faces); architecture should be 'short center, thick radius' with SKILL.md holding only high-signal flow; quality must be maintained like code, with gotchas from real failures being the most valuable; the ecosystem should present each Skill as a feature page, not a repository list; distribution relies on GitHub for cross-platform reach and content platforms for community building, creating a flywheel of articles, products, and use cases. A full lifecycle from real need to feedback iteration is proposed. The article is aimed at AI Agent developers, product managers, and content creators, offering concrete cases and actionable design principles.

x.com · 13 min · Agent Architecture · Agent Skill Repository · AI Engineering

06-30

How To Make Codebases AI Agents Love

This article argues that codebase structure matters more than prompts or AGENTS.md files for AI agent output quality. The core idea is applying 'deep modules' from A Philosophy of Software Design: each module exposes a simple interface controlling lots of implementation. The author introduces 'grey box modules'—developers own and test the interface, AI owns the implementation inside. This improves feedback loops (tests are feedback), navigability (filesystem mirrors mental model), and reduces cognitive load (developers only track 7-8 module boundaries). The article notes TypeScript's difficulty enforcing boundaries and recommends the Effect library. For engineers optimizing AI coding workflows.

www.aihero.dev · 5 min · Agent Architecture · AI Engineering · Code

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

The Orchestration Tax: When Agents Parallelize but Your Attention Doesn't

Starting agents is cheap, but closing the loop (review, merge, judgment) must route through a single serial processor: you. The author frames human attention as the GIL for AI agents — you can spawn 20 agents, but your throughput to main is bounded by your serial review rate. Amdahl's Law applies: the non-parallelizable judgment fraction caps any speedup. Strategies include: scaling agent count to review rate, not UI; sorting tasks into async-able vs. judgment-heavy; batching reviews; making agents prove correctness with tests/screenshots. The insight: busy ≠ productive. Unpaid orchestration tax accumulates both technical debt and cognitive debt, silently eroding your mental model of the system.

addyosmani.com · 9 min · Agent Architecture · Agent Engineering · Cognitive Load

06-27

The New Software Lifecycle: From Writing Code to Judging It

Key insights from a Google whitepaper on how AI transforms the software lifecycle. The core thesis: an agent is 10% model and 90% harness (instructions, tools, sandboxes, orchestration, observability). Context engineering is the primary cost lever, with a critical distinction between static context (loaded every turn, reliable but expensive) and dynamic context (loaded on demand, cost-efficient but needs careful design). Verification determines whether you're vibe coding or doing agentic engineering: tests for deterministic parts, evals for non-deterministic output and trajectory. Real data: one team moved a coding agent from outside top 30 to top 5 on Terminal Bench 2.0 by changing only the harness with the same model; LangChain added 13.7 points on the same benchmark by changing system prompt, tools, and middleware around a fixed model. Implementation collapses from weeks to hours, while specification and verification become the new bottlenecks. For engineers and tech leads adopting AI agents in production workflows.

addyosmani.com · 15 min · Agent Architecture · AI Engineering · Context Engineering

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

15 AI Agent Design Patterns Every Engineer Must Know

This article outlines 15 AI agent design patterns—ranging from single agent, sequential/parallel multi-agent, review/refinement loops, coordinator, hierarchical decomposition, ReAct, human-in-the-loop, plan-and-execute, reflexion, custom logic, to event-driven agents. Each pattern comes with real-world examples, clear when-to-use and when-it-breaks heuristics, and a decision matrix based on the type of uncertainty. The author emphasizes starting with the simplest pattern, capping iterations and costs, logging full action traces, and keeping irreversible actions behind deterministic checks or human approval. A practical guide for engineers building production agentic systems.

x.com · 15 min · Agent Architecture · Design Patterns · Human-in-the-loop

06-24

Loop Engineering: The AI skill every builder needs in 2026

This community-authored article introduces 'Loop Engineering,' arguing that the most effective AI builders are shifting from one-shot prompting to designing automated feedback loops for AI agents. Rather than crafting a perfect prompt, engineers should build systems that discover, plan, execute, verify, and iterate until a verified outcome is reached. It covers six building blocks (automations, worktrees, skills, plugins/connectors, subagents, memory), two loop scales (single-agent vs. fleet), and two types (open vs. closed), while frankly addressing the critical hidden cost of tokens. A practical primer for engineering teams turning AI agents from experiments into production workflows.

x.com · 12 min · Agent Architecture · Agents · AI Engineering

06-23

30 Core Agentic Engineering Concepts Every Developer Should Know

This article distills 20 foundational concepts in agentic engineering, covering building blocks (Agent loop, Think-Act-Observe, state, patterns), configuration (config files, workflow files, prompt caching, context rot), capability (MCP, live document retrieval, persistent memory), orchestration (subagents, agent loops), guardrails (sandboxing, permissions, hooks, prompt injection defense, pre-commit gates), and observability (tracing, metrics). The author argues that frameworks change but these underlying ideas persist; understanding them makes any new tool familiar. Includes concrete config examples and practical advice (e.g., keep config files under 100 lines, distinguish proxy metrics from outcome metrics).

x.com · 24 min · Agent Architecture · Agents · AI Engineering

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

GLM-5.2: Built for Long-Horizon Tasks

Zhipu AI introduces GLM-5.2, a flagship model for long-horizon tasks with a solid 1M-token context and an MIT license. Architecture innovations include IndexShare, which reuses the sparse attention indexer across four layers to cut per-token FLOPs by 2.9× at 1M context, and an improved MTP layer that raises speculative decoding acceptance length by 20% through IndexShare, KV sharing, rejection sampling, and end-to-end TV loss. Agentic RL post-training is backed by the slime framework, and an anti-hack module detects and blocks reward-hacking behaviors like fetching evaluation files or curl-downloading answers. GLM-5.2 ranks as the top open-source model on long-horizon benchmarks such as FrontierSWE (only 1% behind Opus 4.8) and Terminal-Bench 2.1 (81.0), making it relevant for engineers building coding agents and long-context inference systems.

z.ai · 21 min · Agent Architecture · AI · AI 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

06-20

Skillify: turn every agent failure into a permanent structural fix

Garry Tan presents 'Skillify': a methodology that turns every AI agent failure into a permanent structural fix instead of relying on prompt tweaks or apologies. Using two real failures—an agent bypassing a local script for calendar search and doing mental timezone math—he walks through a 10-step verification checklist: SKILL.md contract, deterministic script, unit tests, integration tests, LLM evals, resolver trigger, resolver eval, reachability audit, smoke test, and brain filing rules. This workflow is built into GBrain, an open-source knowledge engine that ensures agent judgment improves permanently and verifiably. Targeted at developers frustrated by recurring agent mistakes.

x.com · 22 min · Agent Architecture · Agents · Ai-Memory

06-19

Imagine Naked People Were Stupider. Naked Models Are.

YC partner Garry Tan responds to Kyle Kingsbury's anti-AI essay, arguing that Kingsbury's tests of naked models are like testing an engine on a bench and concluding cars are unsafe. The article details the 'thin harness, fat skills' architecture: skill files (reusable Markdown procedures) constrain model input, resolvers (routing tables) dispatch tasks, deterministic code handles precision operations, and testing covers the full pipeline. Using Kingsbury's own bathroom rendering and stock data hallucination examples, Tan shows how architecture can turn unreliable models into reliable systems, and shares a personal resolver that reduced file misfilings from 10/13 to zero. The automotive metaphor concludes: seatbelts, traffic lights, and crumple zones made cars safe, not skepticism of engines. Targeted at engineers building or evaluating AI systems.

x.com · 18 min · Agent Architecture · Agents · 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

Stop Giving Every Agent Its Own Skull

Pejman argues that we are replicating a core human limitation—knowledge siloed in individual brains—inside agent systems. Using OpenClaw, Codex, and Claude Code, each agent retains isolated context about him and his projects. The critical gap is not in the repo's artifacts but in the session itself: the debates, dead ends, and pruned idea branches that markdown cannot capture. With literal physical separation across machines, this fragmentation intensifies. The missing layer is a shared, user-owned memory substrate that transcends agent boundaries. He highlights GBrain and CASS as early signals tackling parts of this problem. The piece resonates with engineers building or deeply integrating multi-agent workflows.

x.com · 7 min · Agent Architecture · Agents · Ai-Memory

06-18

Factory 2.0: From coding agents to software factories

Factory announces its 2.0 release, repositioning from individual AI coding agents to an end-to-end 'software factory'. The post argues that improving individual productivity is no longer enough; enterprises need an interconnected, agent-native system that forms a continuous feedback loop from signals (bug reports, customer feedback) through planning, building, testing, reviewing, securing, shipping, and monitoring. Key design principles include model independence (allowing deliberate model choice or automatic routing per task), sovereign intelligence (data plane and control plane options from cloud to fully air-gapped, with all agent sessions and reviews feeding back into the system), and continual learning and self-improvement across the lifecycle. The article lists customers such as NVIDIA, EY, Adobe, and Palo Alto Networks already running software factories in production. Autonomy is described as a gradual maturation process, using simple Droids, skills, automations, Droid Computers, and multi-agent Missions for different levels of human guidance and agent readiness. The piece is a product launch announcement with some technical concepts, targeting engineers and managers interested in enterprise AI engineering and agent orchestration.

x.com · 5 min · Agent Architecture · Agents · AI Engineering

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

06-16

The Context Compression Layer for AI Agents: 60–95% Fewer Tokens, Zero Accuracy Loss

Headroom is a local-first context compression layer for AI agents that slashes token usage from tool outputs, logs, files, and RAG chunks by 60–95% before they reach the LLM, with preserved accuracy. It offers library, proxy, MCP server, and agent wrapper modes, using a content router to select the best compressor for JSON, code, or prose. Reversible compression ensures originals are retrievable on demand. With cross-agent memory and `headroom learn` for mining failed sessions, it is ideal for engineers running coding agents daily and anyone seeking to slash LLM costs without changing their workflow.

github.com · 18 min · Agent Architecture · Ai-Memory · Context Engineering

06-15

Decomposing the agent harness into swappable workers: the iii engine architecture

Mike Piccolo argues that monolithic agent frameworks force a tradeoff by bundling the loop, tools, memory, and orchestration into one block, which long-running teams inevitably rewrite. He walks through the iii engine's production worker stack, where all thirteen harness responsibilities—credential resolution, policy checks, turn FSM, session persistence, budget tracking, etc.—are decomposed into 11 independently replaceable workers. Each worker connects to the engine via WebSocket and registers functions and triggers using a single primitive (iii.trigger()), making the harness a composable set of installable workers. The post provides a step-by-step trace of a turn through provisioning, streaming, policy-gated tool dispatch, and reactive approval wake-ups, alongside concrete examples of swapping the model catalog, adding a provider, or integrating a Slack approval surface. The core bet: an agent harness should be a slider of composable workers rather than a framework you fork. This is for backend engineers building or scaling custom agent infrastructure who are hitting the composability limits of existing frameworks.

x.com · 20 min · Agent Architecture · AI Engineering · Observability

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

06-12

How To Build AI Agents in 2026 (That Actually Work)

This article systematically deconstructs the architecture and engineering practices for building practical AI agents. It clarifies the boundaries between chatbots, AI agents, and agentic AI, emphasizing that a real agent is a system that persistently loops toward a goal rather than delivering a one-shot answer. The author explains the ReAct loop (Reasoning + Acting) and breaks down the five building blocks: the LLM as the brain, tools as hands, short-term and long-term memory, self-correcting loops, and verification. Using a case study of a startup research agent for the fitness niche, the article walks through goal setting, tool integration, loop construction, memory implementation, and the addition of a critic agent, complete with copy-paste system prompts. It highlights six common failure modes and recommends a 2026 tech stack including Claude Code, LangGraph, and MCP. The piece provides a weekend roadmap to build a basic agent from a 50-line Python script and is aimed at developers shifting from prompt engineering to designing agent systems.

x.com · 21 min · Agent Architecture · AI Agents · AI Engineering

06-11

Designing loops with Fable 5: self-correction and cross-session memory

R. Lance Martin demonstrates two loop patterns for Anthropic's Fable 5: self-correction and cross-session memory. On the Parameter Golf challenge (train a model under 16MB and 10 minutes on 8xH100s), Fable 5 with CMA and a verifier sub-agent improved the pipeline roughly 6x more than Opus 4.7, favoring structural changes over scalar tuning. On a continual learning SQL benchmark, Fable 5 progressed through fail-investigate-verify-distill into general rules, reaching 73% verification coverage, while Opus 4.7 and Sonnet 4.6 stalled at sparse notes or uncertain schemas. The key takeaway: design loops and environment feedback so the model can hillclimb, rather than relying on direct prompting.

x.com · 5 min · Agent Architecture · Agents · AI Engineering

06-10

Designing loops with Fable 5: self-correction and memory in agentic workflows

The author shares two practical directions for improving agentic workflows with Anthropic's Claude Fable 5 model: self-correction loops and cross-session memory. In a Parameter Golf challenge—train the best model within a 16MB artifact in under 10 minutes on 8×H100 GPUs—Fable 5 improved the training pipeline roughly 6× more than Opus 4.7 when using Claude Managed Agents with Outcomes judged by an independent verifier sub-agent against nine checkable criteria. Fable 5 bet on larger structural changes and pushed through a quantization regression, while Opus 4.7 stuck to tuning scalar hyperparameters. For memory, the author used a SQL-based task from Continual Learning Bench 1.0 with filesystem-backed memory across agent sessions. Sonnet 4.6 only logged failures and guesses; Opus 4.7 built flagged schema references but verified only 17% of questions; Fable 5 reached 73% verification coverage in the best run and distilled learnings into general rules. Engineers interested in agent architecture and model capability boundaries will find the experiments relevant.

x.com · 5 min · Agent Architecture · AI Agents · AI Engineering

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

How to Design a Loop That Prompts Your Agent

This article presents a loop architecture that enables an AI agent to autonomously complete multi-step tasks by building an automated prompting system instead of manually crafting each prompt. It breaks down the loop into five parts: defining a 'done' check, building prompts from dynamic state rather than hand-fed instructions, executing actions while capturing all outputs, feeding failures back as the next prompt, and setting hard stop conditions like max turns and cost limits. A walkthrough of fixing a login bug shows the loop in action, emphasizing that real costs come from repeated turns, making guardrails critical. Encapsulating repeated operations into reusable skills is highlighted as the key to long-term value, and common mistakes—like lacking an exit condition or discarding error output—are pointed out. Suitable for developers shifting from one-shot prompts to designing agent control flows.

x.com · 18 min · Agent Architecture · Agents · AI Engineering

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