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Tooling Updated UI - Preparing For More Functionality
**This post is for the technical builders** For those who dont use tooling -- its the actual key for enterprise use cases and its the biggest differentiator, even internally, as you chase big projects / clients. We have set up abilities using tools, middleware and external database that you wouldn't even believe and they are very high ticket (think north of $50k), I suggest its something to take a look at if you haven't before So, as we build alongside you for fulfillment we noticed some limitations that I think needed to be addressed. So, here is the basis of those additional functionalities: post / put / patch / get / delete, execution type (proxy, direct, workflow execution), headers, timeout MS, flattened tool menu to a page not a popup Proxied tool calls is your traditional tool call for middleware as you see it today. Output is wrapped in an args object with a meta_data object. These are best suited for middleware usage (make, n8n, buildship, etc) because not only do you have your agent's output but you have location id, contact id, etc. Direct tool calls are exactly that - we dont wrap it in anything - we just send the raw request exactly as you have configured it. This is for when you want to attach to something like an MLS, or niche API directly. Cofigure headers (variable friendly), configure body parameters (and soon query parameters), and the http type. We'll send the request on your behalf just as you would anywhere and return the direct output. Workflow execution is for the internal workflow engine we are adding so we can supply you with a middleware if you dont already have one, or looking for something more native to our functinoality. more to come, happy thursday
Tooling Updated UI - Preparing For More Functionality
0 likes • 9d
Yah this is pretty awesome.!!
0 likes • Oct 8
This is awesome... any reason why we cannot copy and paste hex code in the field?
Best Whatsapp Option?
I'm looking for a solution that allows me to connect with an existing WhatsApp number (from Columbia). Which option do you recommend? - GHL Native Connection - WAGHL - AppLevel WhatsApp - Would love to hear your experiences! Drop your thoughts below. 👇
0 likes • Sep 18
@Ryan B it is one or the other. I use assistable for conversational AI.
1 like • Sep 18
@Ryan B Our main solution is a whatsapp chatbot that runs in ghl triggered by leads from Meta Ad campaigns. For chat-widgets, we use GHL widgets (sadly as they are really crappy looking)
Flow Builder?
Anybody have any videos on how to actually use this? Trying to wrap my head around it and take my bots to the next level.
1 like • Sep 15
Last time I asked, it was not working and I’m not sure it’s been resolved….
JSON content format as a prompt?
I’ve been experimenting with prompt formats and was curious about something. Instead of writing prompts in plain text, has anyone here tried structuring them in JSON format? For example, using keys and values to organize context, instructions, and expected outputs. If you’ve tested this, how did it compare? Did you notice clearer results, different behavior, or weaker performance? Would love to hear your experiences, whether it worked better, worse, or simply different.
JSON content format as a prompt?
2 likes • Sep 11
@Saied Balajio Here's what Claude says: XML prompts offer several key advantages over standard text prompts for AI agents: Structure and Clarity XML provides explicit hierarchical structure through nested tags, making complex instructions much clearer than paragraph-based text. Instead of relying on natural language markers like "first," "second," or "additionally," you can use tags like <step1>, <step2>, or <examples> to create unambiguous sections. Selective Processing AI agents can easily parse and extract specific sections of XML prompts. For instance, if you have <examples>, <constraints>, and <output_format> sections, the agent can process each independently and apply them at the appropriate stages of reasoning. Reduced Ambiguity XML eliminates many interpretation issues common in natural language. When you write <required>Generate exactly 3 options</required> versus "Please generate about 3 options," the XML version leaves no room for ambiguity about what constitutes compliance. Better Instruction Separation Complex prompts often mix different types of instructions - context, examples, constraints, and output requirements. XML allows clean separation: <context>, <examples>, <constraints>, <output_format>. This prevents the agent from confusing examples with actual instructions. Conditional Logic Support XML naturally supports conditional instructions like <if_user_asks_for_code> or <when_handling_sensitive_topics>, making it easier to create adaptive prompts that respond differently to various scenarios. Easier Prompt Engineering XML makes it simpler to modify, debug, and version control prompts. You can comment out sections, reorder components, or modify specific parts without affecting the overall structure. Consistency Across Interactions XML prompts tend to produce more consistent outputs because the structured format helps the AI maintain focus on each component of the instructions throughout its reasoning process.
2 likes • Sep 11
@Ron G TBH it’s no extra work upfront… formulate a solid text prompt and run it through Claude to convert to xml…
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Colin Wall
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@colin-wall
25+ years in marketing & growth. Now helping agencies harness AI + automation to scale smarter inside my AI community. https://skool.com/colin-ai-2907

Active 3d ago
Joined Aug 26, 2024
Surrey, UK
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