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! submitted by /u/DetectiveMindless652
Originally posted by u/DetectiveMindless652 on r/ArtificialInteligence
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