标签 · Orchestration — Glean

Recent picks

19picks · chronological

08-17

Graph Engineering explained: what it is, when to use, when not to

This article frames multi-agent workflow design as 'graph engineering': nodes do bounded jobs with defined contracts, edges carry real outputs, and a 'fake-edge test' exposes unnecessary waits in linear chains. The core pattern is the diamond—fan out parallel research, reduce with plain code, synthesize with one final agent—plus a strict rule that verifiers must run on fresh context, never the worker's chat. It includes copy-paste specs for Claude Code dynamic workflows (triggered by the word 'workflow') covering research, SEO drafting, go-to-market, refactoring, and discovery loops, along with three failure modes: context collapse, false independence, and silent node failure. It also cites honest economics: the Bun rewrite example ran ~50 workflows with up to 64 agents over 11 days, costing roughly $165,000 and requiring heavy human supervision. For engineers already using Claude Code and looking to move from sequential prompts to parallel orchestration, this is a useful but beginner-leaning guide.

x.com · 24 min · Agent Engineering · Agents · Claude Code

08-16

Graph Engineering: from 1 prompt to 100 agents in one system

This article argues that the workflow itself is the engineering target: alongside prompt, context, harness, and loop engineering, there is a fifth layer the author calls graph engineering. The core observation is that default linear agent flows confuse sequence with dependency: most arrows do not read an upstream result, so cutting them is what enables real parallelism. A hundred agents, in this view, are not a hundred roles but one role instantiated a hundred times with its own slice of the problem. The eight-step method covers node contracts, four topologies (chain, fan, router, controlled cycle), when joins are worth waiting for, separating probabilistic classification from deterministic routing, placing independent verifier nodes, and keeping durable state. It explicitly warns that parallel breadth is expensive, citing Anthropic's multi-agent research system consuming roughly 15x the tokens of a normal chat. Useful for engineers moving from a single prompt to a multi-agent production system, though there is no runnable code.

x.com · 10 min · Agent Architecture · Agents · Context Engineering

08-08

Cloudflare ADLC: Workflow-based CI/CD for agent software factories

Cloudflare argues the bottleneck in software development has moved from implementation to every other SDLC stage, now that AI makes code generation cheap. Their answer: let agents drive more of the lifecycle, not just codegen. The post introduces @cloudflare/ci, local OTel traces for Wrangler, Agent Traces, and a set of primitives meant to turn the SDLC into an 'Agent Development Lifecycle' for software factories. It includes Workflow code that parallelizes lint/test/typecheck/build and then deploys, plus guidance that CI/CD is just one kind of Workflow—workflows can spawn containers, agents, and browsers and persist state for days. The article also lists seven platform requirements for agent-driven delivery: programmatic, horizontally scalable, reproducible, push-based, atomic, permissioned, and self-improving. Useful for engineers building agent infrastructure on Cloudflare or exploring autonomous delivery pipelines.

blog.cloudflare.com · 14 min · Agent Engineering · AI Agents · Cloudflare

08-05

What Is an Agent? Agents vs Workflows

This short post clarifies Anthropic's now-canonical distinction between agents and workflows. Both orchestrate multiple LLM calls, but workflows hard-code the sequence and termination condition in developer-written code, while agents hand tools to the LLM and let it decide which to call and when to stop. The post notes that a single LLM call is neither, and argues that workflows often outperform agents on clearly specified, repetitive tasks. Includes simple diagrams contrasting the two. A useful conceptual primer for engineers new to agent architecture.

www.aihero.dev · 3 min · Agents · LLM · Orchestration

08-02

Agentic AI Architectures: Patterns vs. Trade-offs

A practical introduction to agentic AI architecture selection, breaking down components and comparing seven patterns—single-agent, sequential/parallel, router, hierarchical, network/swarm, and generator-critic—with diagram examples and a trade-off table. The article offers two operational tests: introduce a handoff only when it changes required expertise or permissions, and use parallel branches only when they are truly independent. Production concerns include explicit stop conditions, shared-state conflict rules, and tool-failure semantics such as timeouts that must not look like empty results. Also covers common mistakes like adding multiple agents before proving one is insufficient. Suitable as a first-pass checklist for engineers designing agent workflows. The closing sections promote Kimi Agent and its Swarm feature, so the vendor bias is present but the core guide stays relatively neutral.

www.kimi.com · 15 min · Agent Architecture · Agents · AI Engineering

07-23

From Loop to Graph: The Real Shift in AI Agent Improvement

This essay explores the shift from single-loop optimization to graph-structured improvement in AI agent engineering. It dissects four failure modes of single loops: Goodhart's law, blindness to target correctness, conflict among loops, and measurement decay. Mature systems employ graphs of loops—champion-challenger, drift monitoring, rollback, and audit loops—each watching and constraining others. But graphs alone fail without anchors: measures that touch reality, frozen rules, and human judgment on what 'better' means. The key is groundedness, not topology. For AI engineers, MLOps practitioners, and agent designers.

x.com · 13 min · Agent Architecture · AI Engineering · Loop Engineering

07-23

Graph Engineering with Claude: 14-Step roadmap from 0 to graph architect (Full Course)

Most multi-step agents are built as linear chains, each step waiting for the previous one, wasting parallelism and creating bottlenecks. This article introduces graph engineering for agents using Claude Code's dynamic workflows. It presents 14 steps to transform linear scripts into directed acyclic graphs (DAGs) where nodes are bounded work units with contracts (validated input/output schemas) and edges are data dependencies. Key patterns include parallel fan-out (parallel()), fan-in barriers, runtime routing, adversarial verifiers, and convergent cycles. The author provides JavaScript/TypeScript code examples and topology diagrams, showing how to scale agents from a single-threaded line to a coordinated fleet. Targeted at engineers building complex agent systems who want to improve throughput, reliability, and cost efficiency.

x.com · 21 min · Agent Architecture · Agents · Claude Code

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

07-04

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

07-01

Micro-Agent: Beat Frontier Models with Collaboration inside Model API

The vLLM Semantic Router proposes a different take: a router is not just a request dispatcher but an amplifier of model capability. The core idea is to encapsulate multi-model collaboration inside a single model API call. The user sees one model endpoint (vllm-sr/auto), but behind it the router can automatically select a collaboration pattern — from cost-aware escalation (Confidence), parallel aggregation (Ratings), repeated mixture-of-model reasoning (ReMoM), disagreement-as-signal (Fusion), to budgeted micro-agent workflows (Workflows). These patterns are controlled, configurable, observable runtimes, not application glue. Benchmarks on LiveCodeBench, GPQA-Diamond, and Humanity's Last Exam show the closed-model collaboration scheme (VSR Closed) achieving 92.6%, 96.0%, and 50.0% respectively, matching or beating single frontier models like Fugu Ultra and GPT-5.5. This article is valuable because it sinks multi-model collaboration from the product or application layer down to the serving infrastructure layer, while preserving a single model identity. For engineers building inference routing, multi-model strategies, or cost optimization.

vllm.ai · 14 min · AI Engineering · Cost Optimization · LLM

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

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

06-17

How to build a self-improvement loop for your Skills

This article demonstrates a practical approach to building a self-improvement loop for AI Skills using inner and outer agent loops. The inner loop triggers a cloud agent via GitHub Action on each new issue, applying a triage Skill to classify it. The outer loop runs daily, reviews all human corrections (label changes and comments), and generates a diff to update the Skill file, which is then merged back. The author uses Warp's Oz cloud agent platform for issue triage, providing complete code and a sample repo. The pattern is generalizable to code review, bug fixing, and incident response. Suitable for engineers building AI agents who want to improve skill quality over time.

x.com · 4 min · Agent Engineering · Agent Infrastructure · Orchestration

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

Claude Agents & Skills for Investment Banking, Research, PE, and Wealth Management

Anthropic's official reference implementation of Claude agents for financial services, offering 9 end-to-end workflow agents for investment banking, research, PE, and wealth management, along with 8 vertical skill packs and 12+ MCP data connectors. Everything is file-based (Markdown/YAML), installable as Cowork plugins or deployable via Managed Agents API. Designed for technical teams who need ready-made finance AI workflows while retaining full customization.

github.com · 19 min · Agents · Anthropic · Financial-Services

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

06-08

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