u/DetectiveMindless652

Genuine Dilemma,

I have been working with agents for close to 2 years, and I love it. I built something that basically detects agent loops, sends you emails with type of loops and the ability to pause writes, in conjunction with shared memory ability between agents and full time stamped agent logs, with cost analysis for each agent and general performance.

However, I am unsure if I am peddling a dead horse? I launched last month with 250 users, and 60 using it regularly, and 20 everyday. However, I built this based of my experience, however I am just unsure, if ultimately anyone cares enough?

Here is the part I cannot resolve. The 20 daily users feel like proof the problem is real and that I built something that actually works. But people also signed up because something about the pitch landed, tried it, and disappeared without saying a word. That silence might be the louder signal. For example this is an email I just got (I accidentally sent a duplicate email lol)

"I don’t mind emails, I just keep getting duplicates. Sorry if I came off rude. I like Octopoda a lot and think it’s without a doubt the best memory management system I’ve used. I’m having to redesign my workflow now that GitHub has decided to inadvertently destroy their Copilot service (lol) but once I find a new agent system I’ll probably use octopoda again. 

Sent from my iPhone"

so stuff like this makes me think I am genuinely on to something in the agent space, however I have given up a lot of time, money and effort to build this!

I love this community, and find it has always been super helpful, and advice, including just fuck it off, or anything is appreciated my friends!

Am I peddling a dead horse and the lovers are an outlier keeping me delusional? Or are the 198 just normal signup noise that does not actually mean anything about the product itself?

Don't know which crowd to treat as the truth right now.

When I first thought about what agents need, it always came to my mind, oh memory. However, in my opinion the truth after using agents for 18 months is way way way different lol.

Memory is obviously needed, especially the more complex tasks get no doubt. However, for me with out a doubt the missing piece is being able to observe what the fuck your agent is doing and why.

My Repo focuses on five things: persistent memory, loop detection, audit trail, crash recovery, and a live dashboard. Five features, however kind of surprisingly intitially but not now is everyone is fucking with the loop detection, audit trail and performance side of things, i think people are pretty bored with memory lol.

Here's what surprised me. Of the GitHub issues, customer feedback, and Reddit DMs I've gotten over the last few months, maybe five percent has been about memory. The other ninety five percent has been about the observability stuff. Loop detection: does it catch X pattern, how fast does it fire, can it auto pause. Audit trail: can I replay a decision, is it tamper proof, can I see what the agent knew at the moment it acted. Dashboard: can I see all my agents at once, can I export, what about anomaly alerts.

I built memory thinking that was the headline. Turns out the headline is the other four features.

My honest guess about why. Memory is the thing you imagine you need before you ship. Observability is the thing you wish you had after your agent burned through $400 of API calls overnight retrying a tool call that already succeeded five minutes prior. Or after a customer asks "why did my openclaw just text my ex girlfriend instead of doing customer service' and you have absolutely nothing to point at.

Genuinely Curious to see if people feel the same, those who run agents for days or weeks, like is it the hallucination that pisses you off or is the lack of transparency when your agent just does its thing?

I really do feel its not concidence that most people want transparency over memory.

Totally expecting to get roasted here, because its reddit. However, I built something based off my own experiences running agents, and for me its all about saving money, having better control and letting agents work together, rather in parallel.

This is fully local and allows you to basically monitor, store memories, debug and detect 9 different types of loop with email alerts and built in kill switch which is customisable.

In my mind this has been useful, and I am close to storing my 450k memory. However, other agent builders, is this something you would find useful, or is it overkill?

Peace people, and I appreciate any insight given.

Hi Folks,

Hope you are having a lovely day, and wanted to share what I have been working on as i really have tried to model it off what I struggle with agents in the past, ultimately coming down to cost and not knowing what the hell its doing when its bugging out.

Its not perfect, but I would really love to know what people are specifically struggling with agents, is it observation? memory? time debugging?

Let me know, as this would be superhelpful!

[effacé]

I started a saas www.octopodas.com that is essentially AI Agent Observation layer with built in memory, shared memory between agents, advanced loop detection (stop burning money) audit trail (what your agents are doing)

I am a month or so into launch and have 200 users, (not that active) and 250 stars on github.

However, I cannot work out if i am peddling a dead horse or like it is something for every 5 comments i get saying this is great, i get one that says not needed.

My current customers like it.

however, i am feeling pretty low, like should i stop? I have given everything for this a year of hard work, and i am just so uncertain on what to do.

Sorry for the rant!

Hey folks, I've been running a small AI agent infrastructure product for a few months and I keep running into the same problem. It's not agents crashing. It's agents that work but waste money in really subtle ways. The kind of stuff that doesn't show up in error logs.

Like an agent that retries the same prompt on a more expensive model every time it doesn't quite get what it wants. So you go from gpt 4o mini to gpt 4o to gpt 4.1, get basically the same answer, and pay 25 times more. Or two coordinating agents fighting over the same shared key, where Agent A writes approve and Agent B writes reject and they just keep overriding each other forever. Or the model that keeps starting its responses with "actually, wait, let me reconsider" four times in a row on the same prompt, just burning tokens because someone left reflection mode on too aggressive. Or an agent that reads a key, writes back the same value with a tiny phrasing tweak, repeatedly, forever.

LangSmith shows you traces. Helicone shows you cost. Phoenix shows model drift. None of them catch patterns across calls, which is where most of the real waste lives.

So I built one that does. It runs 10 detection rules in real time on the audit trail and tells you which loop you're stuck in plus a copy paste fix.

There's three pages in the recording. The first is Loop Intelligence which shows actual detections firing on traffic from five simulated agents. Each one has the evidence behind it (which calls, which prompts, which costs) and a suggested fix. The second is the Audit Ledger which is a hash chained tamper evident trail of every agent action with cost, model, latency, and prompt hash. Useful for figuring out what the agent actually did at 3am. The third is Atlas which extracts entities and relationships from agent memory and shows it as a graph. Helps debug why an agent knows what it knows.

It also sends you an email when an agent has looped with an option to stop writes and diagnose and the other features:

• Loop Intelligence. 10 real time classifiers for agent failure patterns (cost inflation, ping pong, self correction, polling, decision oscillation, recall write, retry storms, tool nondeterminism, reflection, clarification)
• Audit Ledger. Hash chained tamper evident trail of every agent action with cost, model, latency and prompt hash
• Atlas. Entity and relationship graph extracted from agent memories, visualised in 3D
• Memory Explorer. Browse, search and full version history for every agent memory
• Circuit Breaker. Auto pause agents that exceed your spend rate, with email alerts and per agent thresholds
• Dedup Guards. Prevent agents from rewriting near identical values to the same key
• Recovery. Snapshot and restore any agent's state to any prior point
• Performance. P50, P95, P99 latency on every endpoint, per agent
• Analytics. Token usage, cost trends and agent activity over time
• Apply Fix. One click execution of suggested fixes from any detection
• Framework integrations. LangChain, CrewAI, AutoGen, MCP and OpenAI Agents wired in out of the box

Can you let me know which problems you suffer with and which ones you think are not neccessary?

It also has built in real time agent analytics, memory (boring I know) and shared memory which i like, so agents can read each others memories.

It is a work in progress, and not perfect but I would love to hear peoples feedback, this sub has been awesome for support, and if you do not like it, and think its terrible let me know why it is just as useful.

if you fancy checking it out

www.octopodas.com for cloud

https://github.com/RyjoxTechnologies/Octopoda-OS for local users!

once again thanks for the support folks!

I decided to make this off my personal experience, and likely many others. This post is not written with AI so forgive me if it's not very coherent, nor will I invent a made up story to shill it lol.

Here's an overview without rambling.

Firstly loop detection. When your agent rewrites the same thing too many times, retries the same broken API call, or escalates from cheap models to expensive ones for no reason, it catches it. Shows you exactly which writes were too similar, when, and what they cost. One click to clean it up, reversible for 7 days.

Secondly safety rails. Stops your agent from saving the same memory twice. Set per agent so different agents can have different rules. Define the similarity threshold (default 85%) and the key pattern, and writes that match get blocked at the API. Useful when an agent gets into "wait let me think again" mode and floods your store.

Thirdly the cost kill switch. Per agent dollar per minute thresholds. Your customer support bot might be fine at $0.50/min, but your overnight research agent should hard stop at $0.05. When an agent crosses its own threshold, that one specific agent auto pauses while others keep running, and you get an email naming the agent and the spend that tripped it.

Then a cool Obsidian style memory graph. Real time view of every memory your agents wrote, every decision they made, every goal they had. When something goes wrong you scroll back and see exactly what happened.

Lastly every event your agents take, memory writes, decisions, plan changes, pauses, resumes, gets logged in a tamper evident chain per tenant. If anyone edits history after the fact the chain breaks and the system catches it. Useful if you're in healthcare, finance, legal, or anywhere a customer might one day ask "did your agent really tell me X?" and you need to prove it.

Plus real time analytics on agent activity and built in memory (boring I know).

This is a work in progress and something I have been grinding on for like 6 months. I hope you like it, and if you don't, please let me know why and how I can improve it, really important to me lol.

I appreciate the support, on the whole this community is awesome and super supportive.