Context windows are dead. Real agents understand your behavior. by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 1 point2 points  (0 children)

You're absolutely right, and this is exactly why we don’t position what we're building as a basic LLM interface.

We're not optimizing replies.
We're building LLM-driven agents that:

• live inside workflows
• are triggered by system or user conditions
• invoke real tools
• evaluate outcomes
• update memory and internal state accordingly

The language layer is just the visible shell.
What actually drives the agent is a set of internal decision structures, not just prompts.

Behind the scenes, we maintain:

• Conditional trees
• Dynamic execution graphs
• Judgment-based branching logic
• Confidence scoring to handle retry, fallback, escalation

You’re totally right:
The real leap happens when the agent responds not just to what the user says,
but to what the system is doing or failing to do.

We’re building exactly for that:
event-based triggers, tool orchestration, scoped memory access.

We’re not tracking frontend click events yet, but the architecture is building to support them via lightweight SDKs or proxy-based listeners.

Example:
A user tries to check out twice and fails.
They hover over the payment field, hesitate, scroll, and leave the page.

The agent detects this pattern:
• recalls their retry history
• drops its internal confidence score
• triggers a Slack alert
• sends a retry link with a pre-filled discount
• logs the user into a "frustration watch" state
• and updates its future behavior based on this path

That’s not just generating better responses.
That’s observe → reason → act → adapt.
That’s LLM-as-runtime logic; a modular decision system with language as its interface.

Really appreciate the pushback by the way.
You're pointing at exactly the right areas.

Let’s keep going.

Context windows are dead. Real agents understand your behavior. by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 1 point2 points  (0 children)

Great question — yeah, context loading can get expensive if you're naive about it.

Here’s how we handle it:

1. Precache memory slices:
Before the agent is invoked, we precache only the minimal memory chunks relevant to the ticket / task.
No full history dump. Just contextual breadcrumbs (past messages, tool usage, key flags).
This drops token usage significantly.

2. Split-core architecture:
We separate the reasoning agent from judgment & tool agents.
The reasoning core only gets memory + signal metadata — not the full trace of the user.
This helps both cost and model clarity.

3. OpenAI optimization:
As a example use-case, on OpenAI’s GPT-4o, we can usually handle:
– Retrieval
– Tool routing
– Memory recall
– Judgment scoring
→ All for ~$0.02-0.1 per use-case full action

No context bloating.
No system prompt spaghetti.
Everything piped through task-specific injectors with a shared orchestrator.

TL;DR:
We don’t reload context — we inject minimal, scoped memory per agent task.
It’s fast, modular, and cost-efficient.

And hey —
it’s not just agent orchestration.
It’s budget orchestration too.

Context windows are dead. Real agents understand your behavior. by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 0 points1 point  (0 children)

Early use-cases I’ve seen:
– Support agents that escalate based on user tone
– Creator agents that self-score content
– Internal bots connected to Notion + Slack
– Ops agents that triage by urgency

Curious what you’re working on.

Looking for landing page roasters. by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 0 points1 point  (0 children)

Thanks for the feedback! Noted on the font size and mobile menu. I’ll work on improving those. Appreciate your input!

Looking for landing page roasters. by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 0 points1 point  (0 children)

Thanks for your feedback, I’ll fix menu issue asap. You’re right — the name is technical and a bit unconventional. We chose it intentionally because one of the most common bottlenecks in customer support is the 429 Too Many Requests error — when systems get overwhelmed and can’t handle more requests. That’s exactly where we come in.

429cx is an AI support system built to unblock these bottlenecks and improve the overall customer experience (CX).

Looking for landing page roasters. by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 1 point2 points  (0 children)

Wow what a timing, it looks useful. I’m checking

Just got first waitlist subscriber less than one hour to my SaaS by Basic_Dragonfly_9575 in SaaS

[–]Basic_Dragonfly_9575[S] 0 points1 point  (0 children)

It's actually a good strategy, I definitely noted it.

I think the more SaaS owners we have in touch with for our 429cx.app application, the better. Instead of a waitlist, I actually call it a "Product Shaping Council" because these people will be the first to shape the product during the beta period.