Jeff Joyce

Most B2B companies treat a form fill like the beginning of the buying journey. It usually is not. By the time someone fills out a form, they may have already: - Researched the category - Compared competitors - Read reviews - Talked to peers - Built a shortlist - Decided who they trust So if your sales and marketing motion only starts after the demo request, you are probably showing up late. That is the problem I break down in this video. The real issue is not always lead volume. It is timing.

Salesforce acquiring Fin is worth reading as more than another agent headline. Customer support agents sit close to messy operating state: account context, policy exceptions, refunds, handoffs, SLA pressure, and the CRM record everyone else trusts. If the agent resolves the issue but the state around it is hard to inspect, the work is not really done yet. x.com/salesforce/sta…

@fortelabs Dude I have an entire backlog log of stuff waiting. Hopefully it doesn’t go straight to pay per token.

Anyone else keeping a queue of tasks just for when Fable comes back online?

@dan__rosenthal The Notion-SOP graveyard is real. I’d rather see the agent learn from the work actually happening — tasks closed, fields changed, weird handoffs — than from a doc everyone forgot existed.

@emollick The productive failures line feels important. For company workflows, I’d rather see teams run small agent loops and keep the failure log than pretend the first clean demo proves the org is ready.

We don’t honestly know the best approaches to rebuilding companies around AI agents, especially in ways that expand competitive advantage & augment existing human capabilities. Practical agents are merely months old. Experimentation (and productive failures) will be required.

One browser-agent check I’d want before it touches GTM tools: is this the draft tab or the live tab? A staging landing page, test form, sandbox CRM, and production campaign can look close enough to fool a human. If the agent can’t name the workspace, account, and exact surface it plans to edit, I don’t want it making the change yet.

@emollick The productive failures line feels right. For company workflows, I’d rather see teams run small agent loops and keep the failure log than pretend the first clean demo means the org is ready.

I'm surprised this is still on TBH. lol

@claudeai This thread is useful if the examples include the annoying middle too: what the tool touched, what still needed a person, and which parts were reusable after the first run.

Claude Fable 5 has been out for a couple of days. Some projects people have already built with it:

@BillyJack_J Yep. The send button still needs a gate. I’d rather have the agent prepare the draft, sources, and proposed change, then make the human review step painfully obvious.

The AI ops work that matters for small businesses is not another chatbot in a side tab. It is the control plane behind it: what needs review, what an agent can do, what actually ran, and what proof came out. Agents can prepare the work. The send button still needs a gate.

@odedra_tarun That permission curve is real. I’d let an agent touch a calendar long before CRM fields, payment rails, or customer-facing copy — and each step needs a visible undo path.

AI agents are getting good at tasks. The limiting factor is trust. You'll give an agent your calendar before your email. Email before your CRM. CRM before your bank. The whole agentic future runs on a permission curve nobody's mapped yet.

@AnthropicAI This is a good AI rollout test because the work is real and messy, not just tutorial prompts. I’d want fellows leaving each nonprofit with the boring trail too: what Claude helped with, what still needed a person, and what got reused next week.

We’re launching Claude Corps, a national fellowship program matching people early in their careers with US nonprofits. We'll teach 1,000 people to use Claude, and pay them to use AI to advance their hosts’ missions. anthropic.com/claude-corps

@khemaridh Headless CRM is a fun name, but the real test is whether it remembers the awkward relationship context: last email, promised follow-up, who prefers text, and what should not get automated.

@ngxson Local/no-server is a fun constraint here. The thing I’d check next is whether the browser step can show the DOM state it acted on and where it stopped, not just that it clicked through once.

Hacking a quite fun demo: "agentic browser" via llama.cpp web UI The fun part: NO external server is required, all done via clever tampermonkey trick 😁😁

@MichLieben Memory gets practical fast here. If an agent is writing outbound or content, I want it remembering the positioning, killed drafts, and weird edge cases — not just the last prompt.

@defaiscope Yep. Per-step accuracy sounds great until the task has 40 browser moves and one weird permission prompt. I’d rather see a smaller loop with clear stop points than a fully autonomous run nobody can safely debug.

WILD: 🇺🇸 A browser agent that is 99% accurate per step still fails most of the time. Browserbase engineering lead Derek Meegan breaks down why fully autonomous is the wrong goal, and why security is actually the root of scalability.

@mattybme This is a good early AI job because the mess is already sitting in Salesforce. I’d want the report to separate “interesting anomaly” from “owner/action needed this week,” or it becomes another dashboard.

Asked an AI to review a Salesforce pipeline. Deals with no activity in 5,900 days. One rep carrying 90% of the forecast. Opps open since 2010. It was all there. Nobody had time to read it. crmdigest.io — 14 days free #Salesforce #RevOps

@BrainsAndTennis Vertical agents get interesting when the domain mess is part of the product. For GTM that means weird fields, stale owners, approval paths, and knowing when not to touch the row.

@robin_rrtx @chainlink Agent-readiness is a useful way to frame docs. I’d want examples that show the boring stuff too: auth, failed calls, stale state, and when the agent should stop and ask.

Career update: i've joined @chainlink's AI Enablement team to level up docs and dev ex for both humans and AI agents building with the Chainlink platform. the goal: to be at the forefront of agent-readiness. most devs already use AI in atleast some part of their workflow. Chainlink has long been the industry-standard oracle network powering defi and bringing capital markets onchain (price feeds, CCIP, VRF, compliance engines, etc. ) the world is figuring out how agents transact, verify, and act onchain. Chainlink is where that gets built. you are no longer building platforms for human developers, but also for agents. if an agent cannot easily build with your infrastructure, you've already lost half the battle. My job: make sure every developer — human or agent — can navigate that infrastructure without friction. excited to be here, and for what's ahead and ofcourse to be able to work with @pappas9999 @PriyankGupta03 @andrej_dev and many more talented folks there. LINK EVERYTHING.

@duncanriley @SiliconANGLE This is the kind of boring proof layer I’d want around agents. Not “trust me, it ran” — show the workflow step, what it touched, and the proof before the next system accepts it.

Diagrid brings cryptographic proof to AI agent and workflow execution siliconangle.com/2026/06/11/dia… via @SiliconANGLE

@linear I thought this was a rap or country music video for a moment.

Introducing coding sessions. Linear Agent can now triage issues, investigate the cause, write the fix, open a PR, and bring the code back for review. All shared with your team in Linear.