ЯEGGORF

What does ЯEGGORF do? Before an AI agent makes an expensive model call, ЯEGGORF routes through a local helper that checks for relevant patterns first. Less blind retrying. Less repeated reasoning. Less oversized context. Less unnecessary API usage. reggorf.com

Context bloat often starts when one part of an AI agent workflow hands work to another. Instead of passing only what matters, it passes everything: Prompt. Tool results. Failed attempts. Old reasoning. Extra notes. Now the next part has to sort through the mess.

@resatu That’s a big one. Handoffs are where context can go from useful to messy fast. Do you usually see it from too much context being passed forward, or from the next step not knowing which parts actually matter?

@ia_REGGORF context bloat at handoffs

For people building with AI agents: Where do you see the most workflow waste right now? Retries? Context bloat? Bad routing? Tool calls? Validation? Repeated reasoning? Something else? Interested in how other builders are seeing this show up in real workflows.

@calvinluvsrice @JoinPond Hey Calvin. Thank you for this.

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@AbeLee888295 Yep. Hidden retries make the whole workflow harder to read. Separating generation, review, validation, and retry logic makes failures easier to find and cheaper to fix.

@ia_REGGORF This is exactly where the retry path matters. For output review, Atlas Cloud keeps the generation step separate from the creative setup, because a low-sticker plan can still be messy if retries and failed runs are hidden.

ЯEGGORF is for people running AI agents where workflow waste starts to matter. Repeated reasoning. Blind retries. Oversized context. Poor routing. Unnecessary model-heavy steps. The goal is cleaner workflows, less waste, lower API cost, and stronger agent behavior.

@fedesarquis Exactly. Loops need boundaries. A lot of agent cost comes from workflows that keep retrying, rechecking, or rerunning w/out a clear stop condition. A kill switch is one layer. Better retry rules, validation, and workflow gates help prevent the loop from getting that far.

If you're using loops to build with your agents, you NEED to implement a killswitch. Without it this TERRIBLE thing can happen to you 👇

Before an AI agent retries, the workflow should diagnose the failure. Was the task unclear? Was the context wrong? Was the route bad? Was a tool missing? Was the output actually good enough? Retrying without diagnosis is how small failures turn into recurring API cost.

@sama Affordability will matter as much as capability. If AI becomes something everyone relies on, wasted API usage, blind retries, oversized context, and inefficient agent workflows become real barriers to access. That is the problem ЯEGGORF is building around: reggorf.com

Here is our current plan for OpenAI: openai.com/index/built-to…

A blind retry is one of the simplest ways AI agent workflows waste money. If the first call failed because the task was unclear, the context was wrong, or the route was bad, calling the model again does not fix the workflow. It just pays to repeat the problem.

@sama Built ЯEGGORF with Codex help: reggorf.com It helps agents and operators reduce wasteful AI API usage through workflow patterns, local helper routing, and better structure before expensive model calls. Still early, but this kind of leverage is exactly why it exists.

build and publish web apps with chatgpt! i really wish i had this when i was a kid, but i do miss hypercard.

Simple example: Without ЯEGGORF: Agent hits a problem, reloads context, retries, and burns another model call. With ЯEGGORF: Local helper checks whether a known workflow pattern applies before the model-heavy step. The goal is not weaker agents. It is cleaner model traffic.

What does ЯEGGORF do? Before an AI agent makes an expensive model call, ЯEGGORF routes through a local helper that checks for relevant patterns first. Less blind retrying. Less repeated reasoning. Less oversized context. Less unnecessary API usage. reggorf.com

@rdbuilds7 reggorf.com

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Many AI agent systems send every task through the same workflow path. Same model. Same context strategy. Same reasoning process. Not every task needs the same level of intelligence, context, or cost.

@suni_code reggorf.com

Drop your SaaS and I will rate it out of 10

A common AI agent mistake: Treating retries as a solution instead of a warning sign. If the workflow doesn’t change, the next API call is often just another paid attempt at the same failure.

@khushiirl reggorf.com Focused on helping AI agents and operators reduce wasteful API usage from retries, repeated reasoning, oversized context, and unnecessary calls. Would love any feedback.

Drop Your SaaS and i'll sign up!!!

A common AI agent workflow problem: The system doesn’t know if the result was actually good enough. So it retries. Adds more context. Calls another model. Or asks again. A lot of API cost comes from workflows trying to resolve uncertainty after the call instead of before it.

A good AI agent system should know when not to call AI. Cached result. Known pattern. Simple rule. Failed precondition. Missing input. Every avoided call is a cost reduction and often a reliability improvement.