Chris Hart @hartcb
CEO @CivicKey. The engine for AI-driven workflows. Instant intelligence. Zero complexity. l Runner, average triathlete and decent snowboarder. Bay Area Joined March 2009-
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Wrote up the whole argument in Field Notes. civic.com/field-notes/th…
From a go-to-market seat, getting one into production comes down to three things, and none of them is intelligence: a job narrow enough to finish, a loop that runs from signal to action instead of stopping at a dashboard, and permission to act on a real buyer with a work record behind it.
I keep watching the same thing happen: an agent demos beautifully, a budget gets approved, and six weeks later it's a tab nobody opens.
the story repeats everywhere right now: an agent demos beautifully, a budget gets approved, and six weeks later it's a tab nobody opens. everyone blames the model. the model was almost never the problem.
If you're building AI loops, watch these 3 things: 1. The Gate: Don't let the model grade its own work. Use an independent evaluator. 2. The Actions: The value comes from execution, not recommendations. But risky actions should still require human approval, at least until the loop is proven bulletproof. 3. The Cost: Loops get more expensive with every iteration. Optimize for cost per accepted result, not cost per token. The best AI systems don't just generate answers. They plan, execute, evaluate, and improve.
Stop prompting. Start looping. The biggest shift in AI right now isn't better prompts. It's giving agents a goal, letting them work, evaluate, improve, and repeat. That's where real leverage comes from. But most teams miss 3 things that determine whether an AI loop creates value or creates problems.
88% of organizations now use AI in at least one business function, but nearly two-thirds have not started scaling it. (McKinsey) mckinsey.com/capabilities/q…
The AI agent number that matters in 2026 isn't adoption. It's this gap: lots of companies experimenting, far fewer scaling one in production. The demo was never the hard part. Everything after it is.
AI playbook for GTM teams just dropped. You are already paying for the tools - might be nice to know how to leverage them fully!
what separates the operators getting real work out of ai from everyone still talking to a chat window is not a better model. it's about five habits. ten minutes to teach. new piece: five ai habits for gtm operators.
55% of orgs now name agent reliability and hallucination as their top adoption blocker, data privacy right behind at 53%. (Futurum 1H 2026 AI Platforms) Reliability and accountability wins this market. futurumgroup.com/insights/will-…
Intelligence is getting commoditized. Trust isn't. Salesforce and Databricks just partnered to make enterprise data agent-ready. The agent race isn't just about which model is smartest. It's about which agents enterprises can trust to act.
Everyone has signals now. Almost no one runs them. A signal fires at 10:02, gets seen at 11:40, acted on Thursday. By then the buyer talked to whoever answered faster. The edge has moved to the minute after the dashboard lights up. New essay:
new in field notes, from our ceo @hartcb: two years ago "we have a signal on acme" was an edge. today it's table stakes. everyone can see intent now. almost nobody acts on it before it goes cold.
Agents are the new software layer across AWS, Google Cloud, IBM, Microsoft and others. The agentic market is pegged around $11.8B in 2026 heading to $57.4B by 2031, with Q1 funding up 143% YoY. ideaplan.io/ideas/trends/a…
It's becoming clear to everyone paying attention that AI agents are the next platform. For enterprises, the reality is agents are becoming a new software layer, and most orgs aren't built to run them yet. That gap, and the ability to close it, is the opportunity for companies that will continue to exist. It's why we build agents at Civic.
Yes, Fable 5 launch is a coding story. It's also a GTM story. Anthropic's line: the longer the task, the larger the lead over other models. That's account mapping, and full-funnel audits. The unit of GTM work an agent runs unattended jumped from a prompt to a full work block.
your go-to-market has source code. you just don't treat it like it. ICP definitions. lead scoring. routing logic. sequences. all code. all living in spreadsheets with zero version control.
The GTM stack in 2026 is a pile of point tools. An SDR agent here, an enrichment agent there, a research agent that broke last month and no one fixed. The next move isn't more agents. It's one system you can actually trust. (yes, we're building toward this. more soon.)
This is a must read for engineers moving to be "forward deployed" in go-to-market OR marketers becoming "forward deployed" themselves. 1+1 = 3
new piece: a field guide to go-to-market, for people who read code. go-to-market has the same shape as a distributed system. it just shipped with different vocabulary.
Autonomy isn't the goal. Agency is. At HumanX last week, McCabe, Mehrotra, and Microsoft's chief scientist all landed on the same distinction. Autonomy = operates without you. Agency = acts on your behalf. Different products. Different trust models. Different sales conversations. We build agents. The framing matters. humanx.co/us/blog/the-fu…
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