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SLAPPING A CHATBOT ONTO A LEGACY CRM DOES NOT MAKE IT AN AGENTIC PLATFORM Real GTM work requires deep context. Lightfield just raised $45M from a16z to solve this. Watching the demo shows what an agentic platform should actually look like 👀

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CRM has always been weird. Humans do the work → then manually update the CRM about the work. Lightfield is flipping that model. Instead of asking reps to keep the CRM updated, it builds the customer context from calls, emails, Slack, LinkedIn, etc. Then agents can actually work from that context. This feels much closer to what an AI-native CRM should be. $47M Series A led by a16z is a pretty strong signal too.

CRM has always been weird. Humans do the work → then manually update the CRM about the work. Lightfield is flipping that model. Instead of asking reps to keep the CRM updated, it builds the customer context from calls, emails, Slack, LinkedIn, etc. Then agents can actually work from that context. This feels much closer to what an AI-native CRM should be. $47M Series A led by a16z is a pretty strong signal too.

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Onyx just hit #1 on GitHub trending. Open source AI platform — self-hostable, works with every major LLM provider, and ships with: - Agentic RAG - Deep research mode - Custom agents - Web search - Code execution - Voice mode - Image generation - 50+ connectors out of the box This is what a self-hosted AI stack is starting to look like.

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if your GTM teams are still manually updating the CRM, building audiences, doing research, and drafting emails - you're ngmi Ramp Revenue is our internal GTM Coworker used by >90% of all our GTM teams. background agents do real work now: managing inboxes, prospecting, CRM updates, post-call follow-ups but automating repeatable tasks isn't the point (though our sellers do love not updating the CRM manually anymore). the real unlock is that we're encoding institutional GTM knowledge that used to live in reps' heads - playbooks, account memory, what worked for which segment, why a deal moved and making it computable every run compounds it. every human correction teaches the system. the org's rate of learning becomes the moat, not any single agent a few things that made it work: → composable context layer. give an agent a live, shared view of the account and let it write back what it learns. generic models don't know your customers → reliable background work needs a real harness. short episodes, memory, triggers, evals, human approval points. "ship an agent" is easy - doing real work at an acceptable quality bar is hard → playbooks as the orchestration layer: who qualifies, why now, the message, the channel - agents turn that into the actual work where this goes: agents drive experimentation and orchestration - propose their own plays, kill what's not working, and get sharper with every interaction

if your GTM teams are still manually updating the CRM, building audiences, doing research, and drafting emails - you're ngmi Ramp Revenue is our internal GTM Coworker used by >90% of all our GTM teams. background agents do real work now: managing inboxes, prospecting, CRM updates, post-call follow-ups but automating repeatable tasks isn't the point (though our sellers do love not updating the CRM manually anymore). the real unlock is that we're encoding institutional GTM knowledge that used to live in reps' heads - playbooks, account memory, what worked for which segment, why a deal moved and making it computable every run compounds it. every human correction teaches the system. the org's rate of learning becomes the moat, not any single agent a few things that made it work: → composable context layer. give an agent a live, shared view of the account and let it write back what it learns. generic models don't know your customers → reliable background work needs a real harness. short episodes, memory, triggers, evals, human approval points. "ship an agent" is easy - doing real work at an acceptable quality bar is hard → playbooks as the orchestration layer: who qualifies, why now, the message, the channel - agents turn that into the actual work where this goes: agents drive experimentation and orchestration - propose their own plays, kill what's not working, and get sharper with every interaction

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Agents should not discover their authority halfway through a negotiation. It should be set before the negotiation begins. Kite Agent Passport 2.0 shows what governed agentic commerce looks like in procurement: ▷ A person defines the target price, the limit, and the terms that stay protected. ▷ Both agents negotiate within their own private mandates, with every offer checked before it is sent. ▷ Only a package permitted by both mandates reaches human approval and signature. Kite Agent Passport does not replace the human signer. It makes clear what an agent can and cannot do before it acts. Watch the procurement demo. 🪁

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