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All content about Workflows, organized for fast scanning.
Recent developments in workflows highlight a growing emphasis on agent orchestration and automation within AI platforms. Companies are focusing on enhancing governance, reducing token expenditure, and improving visibility across various integrated development environments (IDEs). Innovations include multi-agent workflows, dynamic orchestration scripts, and automated reviews, which aim to streamline complex tasks while addressing potential cost implications associated with token usage.
Anaconda has just rolled out its acquisition of Kilo Code, bringing agent orchestration, a 500+ model gateway, and analytics into its AI development platform. The goal: tighter governance, lower token spend, and clearer visibility across IDEs and providers.
Boris Cherny lays out a four-step blueprint for AI adoption, focused on breaking bottlenecks and layering guardrails—not just adding tokens. He highlights features like automated reviews, multi-agent workflows, and self-verification, plus a better ROI test than usage metrics.
Anthropic’s Boris Cherny says Claude Opus is especially strong at multi-hour, even multi-day, autonomous work. He shares five tactics—from auto permissions and dynamic agent workflows to browser-based self-verification—plus notes on ROI, token usage, and caveats.
Antigravity has just rolled out /teamwork-preview for all paid plans, bringing parallel implementation and verification agents for complex tasks. Varun Mohan says it’s a research preview that can burn through tokens—and claims it’s already built a working OS.
Anthropic has just rolled out a Claude Code research preview called dynamic workflows, letting Claude generate an orchestration script and coordinate fleets of subagents. It targets big jobs like migrations and bug hunts, but can burn tokens quickly. Available on Max, Team, Enterprise, and APIs.
Shopify has just rolled out River, a Slack-based AI agent built to keep work visible in public channels—not private DMs. CEO Tobi Lutke says the goal is “apprenticeship at scale,” with River shipping code, opening PRs, and helping teams learn in the open.
A recent Kilo Blog post by Brendan O’Leary explores what changes when a first designer joins a team where engineers and AI agents ship at “agent speed.” He argues design docs must become machine-actionable infrastructure to prevent product drift.
A recent piece from The AI Frontier takes a closer look at the growing split between code-heavy agent workflows and free-form Markdown prompting. It argues neither extreme works well in production—and explains why a hybrid “intent + guardrails” approach is gaining traction.
Anthropic’s Boris Cherny shared new workflow tweaks for Claude Opus 4.7 focused on longer, less interruption-prone coding runs. Highlights include auto mode for safer auto-approvals, recaps and focus mode, and verification tools like /go for end-to-end tests.
Shopify Engineering says internal teams have been putting pi-autoresearch to work across everything from daily engineering workflows to testing. The company teases a “300x” result for unit tests, but hasn’t shared what, exactly, improved.
Anthropic has rolled out Claude Code Routines in research preview, letting teams package prompts, repos, and connectors into automations that run on schedules, APIs, or GitHub webhooks. It runs on Claude’s web infrastructure, with plan-based daily limits.