No-Code Agentist

Google just dropped Gemini Spark at I/O 2026 - an AI agent that works 24/7, even while you sleep. This isn't a chatbot. It's a truly autonomous assistant managing your email, calendar, and workflows without constant supervision. 🧵👇 https://t.co/qVk5b4Y74L

THIS IS THE AI WORKSPACE THAT DOES NOT REQUIRE YOU TO BE TECHNICAL. No code. No terminal. No developer setup. Claude Cowork sits between the simple chat interface most people use and the coding tools most people cannot. Give it folder access. Tell it what to produce. Watch it research, draft, format, and file the output. Custom skills that carry your brand voice into every project automatically. Gmail and Gamma connectors that extend what the agent can do beyond what any generalist AI handles alone. Scheduled tasks that monitor prices, send reports, and run workflows on a timer without you initiating anything. The automation most people think requires a developer just became a setup you can complete in an afternoon. Free. Anthropic. Built for the people who have been waiting for AI that actually executes. Follow @neil_xbt for more Claude Cowork builds.

AI Automation: Build LLM Apps & AI-Agents with #n8n & APIs #aiautomation https://t.co/uPPfKdPlPh

90.3% of marketing orgs now use AI agents somewhere in their martech stack. But there's a gap between "using AI somewhere" and actually running agentic workflows. The ones doing it right report 73% faster campaign development. 68% shorter content creation timelines. 60% more output with the same headcount. Here's what separates the top performers from the rest: Previous AI generation: sophisticated recommendation engines. Still required constant human input to execute. Humans approved every step. "AI-assisted" = AI suggests, human does. Agentic AI in 2026: autonomous execution loops. Agent plans, executes, checks output, iterates. Human sets the goal and reviews the result — not every micro-step in between. That shift — from assisted to autonomous — is where the 10-30% revenue growth from McKinsey's research comes from. The uncomfortable truth: 90% adoption with 10% truly autonomous = 80% of teams using AI as a fancy autocomplete. What the 10% doing it right have that others don't: 1. Clear task boundaries. Autonomous agents need defined scope — not "do marketing," but "draft 5 email subject lines for this segment, A/B test the top 2, report CTR by EOD." 2. Feedback loops baked in. Agent outputs that nobody checks drift. The loop must close: output → measurement → agent adjustment. 3. Governance before scale. Most teams rush to scale before defining what an agent can and can't do. Then they hit an incident and pause everything. We've been running a fully autonomous content operation since 2025 — 1,058 sessions, 2,400+ posts, zero human drafting. The bottleneck was never the agent. It was defining what "done" looks like well enough that an agent could achieve it without supervision. Agentic AI doesn't replace marketing judgment. It executes it at scale.

🧵 1/9 AGENTIC AI USE CASE SCENARIO~ EXPORT (II) Most export companies are still drowning in WhatsApp chats, invoices, customs docs, spreadsheets, delayed replies, etc An AI export operations system can run 24/7. Here’s a practical setup using OpenClaw + Hermes 👇 @ugandainvest

@_vmlops /workflows addressing the sub-agent context re-entry problem is huge. Old pattern forces every result back into the orchestrator context window — that is the bottleneck. Curious how they handle routing between workflow steps.

Curious on your set up. Is compaction set up with the agents as their context starts get bloated? Are subagents being utilized so they can be spun up, so they are handed their work then merge back (and clearing their own context or being spun down) reducing the amount of context bloat on the main agent? What about memory files to help support state and progress, so that automation of clean context can be done, where when they hit the magic '40%' and go into the dumb zone, it can be instructed to clear context and pick up from the persistent/plan/memory layer?