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Correcting an input — e.g., saying 'actually it's not this number, we will get no fewer than 150 guests' — gets your harness to rethink the plan, see the consequences of that input into other stuff, and suggest either a modification of your plan or just changing the input. You are well within your right to correct information and add context after a decision has been made.
Design something with the intent that 'this really needs to be an aside' when you have too many questions you don't want contaminating your main analysis; the speaker notes that having too many 'stupid questions' leads to opening a separate chat because you don't trust it won't contaminate what you're doing.
When there are a few possible values for something, ask the agent to do three scenarios and review them side by side before making any decision.
Use the 'build your own' file in the repo — it has all the principles baked in, in order, and will guide you on building a similar harness.
Add a curveball to the agent by typing it in the chat and see how some of the evidence lines or inputs that go into the rest of your process change; the evidence lines should trace back to you correcting a fact.
When AI agents can submit at massive scale, human review-in-the-loop systems (security alerts, customer support, college admissions, judicial small claims) get DDoSed and all such systems need to be reevaluated.
For existing Claude Managed Agents integrations, the easiest low-hanging fruit is to move the permission policy from 'always ask' to auto mode.
You can pull a flow transcript via the API and pass it to a Claude Managed Agent prompted with 'here's the things that I want you to look for that happened', or hand it the day's sessions and ask 'was there anything odd that happened in these sessions?'
You can prompt a flow with the desired outcome, a rubric, and a budget together — 'here is the outcome that I want and the rubric that I expect, and here is a budget — go accomplish this outcome given this budget.'
For human-in-the-loop flows, you can give Claude a custom tool named 'request human feedback' so the flow can ask a human for input during execution.
Monitor everything in real time via a durable event log; a single event stream gives you everything agents are doing, and you can steer agents by stopping them via interrupting through the API surface.
New information can be injected into a running flow, e.g., telling Claude 'Lemma X is actually probably useful for you, Claude. Take a look at that.'
A comprehensive set of events is emitted as the session streams; access them programmatically to see every tool call, result, and handoff between sub-agent and lead agent, and inspect the thread message received payload to deeply understand what the lead agent passed to the sub-agent at every transition in the graph.
A running flow session can be interrupted to stop it and redirect it, for example by telling it 'you're going the wrong direction, please stop right here.'
A flow can be interrupted mid-process from the API by passing the 'user interrupt' event, which stops the flow so you can reprompt Claude or otherwise continue.
Platform-side costs to reach the dashboard and account map output: Lovable about $2 (likely because it picked more cost-effective models), Replit about $12, and Claudeforce about $15 in Claude usage, which is included in the $200/month Claude Max plan; Salesforce contract costs are excluded since everyone's is different.
Claudeforce is Claude's new connector to Salesforce, currently in beta with a wait list, built on MCP rather than the standard Salesforce API, and it includes five pre-built skills that ingest Salesforce data so users don't have to figure out common tasks from scratch.
The host argued the case for Replit and Lovable is that they can connect to other apps, whereas ClaudeForce, as cool as it is, is basically useless for his team because it cannot incorporate integrations outside Salesforce the way the other two can.
The hat is symbolic of what dads go through; it signals 'we see you, we feel you' and sparks conversations that wouldn't normally be had, sometimes ending with 'where'd you get that hat?' and a new sale.
The team downloaded all chemicals from all chemists at ChEMBL who published more than 30 papers and 600 molecules, yielding 1,815 chemists. A classifier predicting the chemist from the molecule alone achieved 60% top-five accuracy, which the guest calls a very easy problem. Models get confused distinguishing chemists who ran the same publications and were at the same institution.
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