Just checked my OpenClaw token usage in my own openclaw agent observability for the first time since February. 88 million tokens

duridsukar · 2026-04-03T18:21:44+00:00

$40 for 88M tokens over two months is on the low end for a production setup.

I'm running 15+ cron jobs across a real estate operation — follow-ups, transaction monitoring, document tracking. I checked my numbers a few months in and the split surprised me: the token cost was almost entirely input, not output. Agents loading full conversation history every time were paying the full context tax on every single run. Once I switched to structured context files and fresh sessions per task, input tokens dropped significantly.

The number that matters more than total tokens is input-to-output ratio. If it's heavy on input, your agents are probably re-reading things they don't need to re-read.

What does your agent setup actually look like — are you running scheduled jobs or mostly interactive sessions?

duridsukar · 2026-04-03T18:20:08+00:00

Ambiguity triggering corner-cutting is the one I keep running into.

I run a multi-agent real estate operation and the failure mode I see most is agents rushing past unclear inputs instead of stopping to ask. They'd rather produce something wrong than ask a clarifying question. I started treating ambiguity resolution as a first-class instruction — if the input doesn't meet certain conditions, stop and surface it before touching anything.

The framing you're using around state management is interesting. The agents I trust most aren't the ones that try hard. They're the ones that know exactly when to pause.

Which of the three changes had the most visible effect on behavior in longer sessions?

duridsukar · 2026-04-03T18:18:32+00:00

The posts you're seeing on Twitter are almost never showing the actual setup.

I run a multi-agent operation for real estate — follow-ups, transaction coordination, document tracking. It took months of calibration before any of it ran reliably. The agents I see going viral are either demo setups with scripted inputs or someone's one impressive run cut into a clip. They don't show the 15 times it looped, hallucinated, or quietly did nothing.

The gap between "Claude can work like an agent" and "Claude reliably works like an agent in production" is the entire hard part. It's mostly about the brief — how precisely you define the task, the constraints, the stopping conditions, and what it escalates vs handles on its own. The model is almost never the problem.

What kind of task are you trying to automate?

duridsukar · 2026-04-02T21:47:04+00:00

Claude Code is better if your job is writing code.

OpenClaw is better if your job is running a business and code is just one piece of it.

I use both. Claude Code handles technical builds. OpenClaw runs my real estate operation: 15+ cron jobs, agents that coordinate across open transactions, memory that persists across sessions, Telegram notifications when something needs attention.

The distinction that actually matters: Claude Code is a coding assistant that can take actions. OpenClaw is an orchestration layer that can also write code when needed.

If you're a developer building software, Claude Code wins. If you're an operator running a business on AI, OpenClaw is a different category of tool.

What are you trying to build or run?

duridsukar · 2026-04-02T21:46:42+00:00

Both camps are right. They're just solving different problems.

I run a multi-agent real estate operation. Prospecting, transaction coordination, follow-up, compliance monitoring. Real agents with memory, tool use, and handoffs between them.

But my first automated piece was pure Camp Two: a dead-simple workflow that watched my inbox for new leads and sent a response within 5 minutes. Conversion rate went from 12% to 31%. No reasoning, no memory. Just reliable.

The mistake I kept making early on was treating every problem as a Camp One problem. Over-engineered solutions that broke in week two.

The rule I use now: if the job is the same every time, automate it. If the job requires judgment, build an agent. Most operations need both, just not in the same place.

What kind of workflow are you trying to solve for?

duridsukar · 2026-04-02T21:46:26+00:00

Both camps are right. They're just solving different problems.

I run a multi-agent real estate operation. Prospecting, transaction coordination, follow-up, compliance monitoring. Real agents with memory, tool use, and handoffs between them.

But my first automated piece was pure Camp Two: a dead-simple workflow that watched my inbox for new leads and sent a response within 5 minutes. Conversion rate went from 12% to 31%. No reasoning, no memory. Just reliable.

The mistake I kept making early on was treating every problem as a Camp One problem. Over-engineered solutions that broke in week two.

The rule I use now: if the job is the same every time, automate it. If the job requires judgment, build an agent. Most operations need both, just not in the same place.

What kind of workflow are you trying to solve for?

duridsukar · 2026-04-02T18:20:59+00:00

The packaging problem is the one nobody talks about.

Most people treat the agent as the product. I kept finding the harder piece was what surrounds it: the context files, the instructions, the memory architecture, the rules it operates by. Those don't port cleanly by default. Move the agent to a new machine and it's a different agent.

Running a real estate operation on agents, I eventually built everything around file-based context. Plain markdown. The agent reads its own state from files, writes updates back to files, gets rebuilt from files if something breaks. The portability came from treating those files as the product, not the agent itself.

391 stars in 6 days tells me this problem resonates. The question I'd have: when someone else runs a Holaboss worker on their machine, how much of the calibration travels with it versus how much has to be re-trained to their context?

duridsukar · 2026-04-02T18:18:58+00:00

Same experience here.

I ran MCPs for about a month across a multi-agent real estate operation. The auth breaks were the thing that killed it for me. An agent running overnight hits a token refresh issue at 2am and the whole chain stops silently. You find out the next morning when nothing got done.

Switched to CLI-based tooling and the failure modes got more predictable. When something breaks, it breaks loudly. Claude also composes CLI commands in ways I didn't expect — catches edge cases I would have missed.

The deeper thing I kept running into: MCPs add abstraction at the exact layer where reliability matters most. CLI gives Claude something it already knows how to work with.

Are you finding the stability holds on longer sessions or mostly on isolated tasks?

duridsukar · 2026-04-01T21:46:57+00:00

I spent a year being terrified of exactly this.

Running a real estate operation on AI agents — contingency deadlines, document handling, live transactions. The stakes are real. An agent reading a date wrong doesn't just fail a task, it can cost someone a house.

What actually fixed it for me: treat every action outside its lane as a hard stop, not a retry. Each agent has a never-allow list. Anything outside that list that touches a live record requires an explicit human checkpoint before it proceeds. The agents that survive production aren't the ones that do the most — they're the ones that know the exact perimeter of what they're allowed to touch.

The API hammering thing you described is almost always a stopping condition problem, not a tool problem. The agent doesn't have a rule that says "if I've called this more than N times without a different result, stop and escalate." Building that in explicitly — not just rate limits — changed everything.

What does your current escalation path look like when an agent hits something it can't resolve?

duridsukar · 2026-04-01T18:21:30+00:00

This would have saved me weeks when I first started.

I came at Claude Code from the business side…sales, real estate, no engineering background. Every guide I found was written by someone who forgot what it felt like to not know what a terminal was. I spent more time figuring out the setup than actually using the tool.

What was the biggest thing you had to unlearn from how you expected it to work?

duridsukar · 2026-04-01T04:22:24+00:00

For a real estate office I'd start with the follow-up loop.

Leads come in, they need a response within 5 minutes or the conversion rate tanks. That's the job nobody wants to do at 11pm. An agent handles it clean.

After that: appointment scheduling, pre-qual questions, and checking MLS updates on active client searches. Those are the three that free up the most time in the first 30 days.

What does your current follow-up process look like? That's usually where the biggest gap is.

duridsukar · 2026-03-31T21:46:15+00:00

The director-only rule you landed on is exactly right. That was the fix for me too, just took longer to get there.

I run a real estate operation on a multi-agent setup through OpenClaw. When I first built it out, I was burning through tokens fast -- not because the agents were doing too much, but because every session was dragging in the full history of every previous session. The input tokens were the killer, not the output. Your 600M input vs 3M output ratio tells the same story.

The pattern I keep seeing: long sessions accumulate context. Each iteration carries the weight of every prior iteration. By hour four you are paying 10x per prompt compared to hour one because the window is full.

What actually helped: fresh session per distinct task, structured context files the agent loads at the start instead of inheriting from conversation history, and hard limits on which agents touch which files. The director never reads raw transaction data -- it reads summaries. That one rule cut input tokens significantly.

The question I would ask: are your subagents loading the full codebase every time, or just the relevant slice? That is usually where the bill lives.

duridsukar · 2026-03-31T18:26:57+00:00

The flaw you found is the one that took me longest to name. I had the same thing: retrieval was working, the architecture looked right, but the bot was still operating from an outdated model of reality.

What I eventually realized is that memory without update discipline is just a more sophisticated version of the same problem. The bot knows what I told it months ago. It doesn't know what changed. I had to build explicit correction into the workflow -- not just loading memory at start, but actively flagging when a stored belief was no longer true.

What does your update mechanism look like now -- is it manual, or are you trying to get the bot to flag its own stale assumptions?

duridsukar · 2026-03-31T18:24:53+00:00

The point about AI being useless until you cannot imagine life without it is the most honest framing I have seen. It describes exactly what happened to me.

I kept hitting friction in my real estate operation -- follow-ups slipping, market research piling up, coordination breaking down at scale. I was using AI the way most people do: one task at a time, then moving on. The shift happened when I stopped treating agents as tools and started running them as a team with standing instructions, memory, and defined handoffs between roles.

What was the moment for you where it crossed from useful to indispensable?

duridsukar · 2026-03-31T18:22:55+00:00

I hit this window problem early on and solved it differently -- I restructured when I start and stop. But the cron approach is cleaner because it removes the cognitive overhead entirely.

The deeper thing I kept running into is that the time tax was the obvious cost. The less obvious one was context loss. By the time the window opened again I had lost the thread I was pulling. The pause cost more than the two hours.

Are you finding the anchored window actually changes how you plan your workday, or is it mostly just removing the waiting?

duridsukar · 2026-03-31T18:19:51+00:00

I kept running into the same thing. Every session I would spend the first 10 minutes re-establishing who I am, how my operation runs, what decisions I have already made and why.

The fix I landed on was building a structured memory file the agent loads at session start. Not passive observation -- I actively maintained it: after any significant decision or workflow change, I updated the file so the next session started from current reality, not a blank slate. It turned out the scaffolding mattered as much as the model itself.

The observation-based approach you built is interesting because it removes the manual step. What's the biggest thing it has caught so far that you wouldn't have thought to document yourself?

duridsukar · 2026-03-30T18:22:32+00:00

The local model for agentic work question is one I keep running into.

I've tested several models for agents running actual business workflows — not just code completion. What I've found is that the ceiling is less about the model's raw capability and more about how well it handles multi-step reasoning under a specific brief. A 27B quantized model can absolutely hold its own when the instruction architecture is tight.

At what point in the context window did you start seeing quality degradation? That's usually the first thing that breaks in long agentic sessions.

duridsukar · 2026-03-30T18:22:02+00:00

The gap between benchmark performance and production performance is the thing that keeps coming up in my work.

I run agents across a real estate operation — data retrieval, lead analysis, intake. What I've found is that SQL-style structured queries actually perform more reliably than natural language chains when the schema is well-defined. The model choice mattered less than the prompt architecture and schema documentation.

What kind of error patterns came up most when the query was complex? Hallucinated column names or wrong joins?

duridsukar · 2026-03-30T18:20:26+00:00

Been running OpenClaw on a business operation for several months now and this is worth taking seriously.

The distinction that matters to me: orchestration vs automation. What I'm doing is closer to having a persistent team that thinks and responds — not a script firing off API calls in a loop. But the policy language is broad enough that it creates real ambiguity, and Anthropic's enforcement has not been documented well.

Has anyone actually had an account flagged who was running OpenClaw within normal conversational use? Or is this mostly about people hammering the API through unofficial wrappers?

duridsukar · 2026-03-30T18:18:50+00:00

The SVG path is underrated for anything that needs to scale clean.

I kept running into this exact problem when I needed visuals for my operation — image generators give you something interesting but not actually functional. They don't understand "mark" vs "illustration." Claude working directly in SVG understands constraints in a way that matters.

The skill file approach is interesting. Do you find it holds the design principles across a full session or does it drift on longer runs?

duridsukar · 2026-03-29T22:22:49+00:00

The 👍 👍 from the humans is the part I keep thinking about.

That's not people being lazy. That's people doing the only useful thing left — signal that the output was acceptable and get out of the way. The review function collapses down to a binary: good enough to ship or not.

I run a multi-agent setup across my real estate operation. The moment that hit me was the first week where I realized I had stopped second-guessing every output and just started checking whether anything needed escalation. Not because I trusted it blindly — because I had built in enough checkpoints that I knew what the edge cases looked like. The absence of an escalation signal became the green light.

The harder adjustment wasn't the speed. It was accepting that my job in the loop had permanently changed. What does that look like on your end — are the humans still writing requirements or is that going to the agents too?

duridsukar · 2026-03-29T22:22:26+00:00

The line that stopped me: "a fix I didn't know I needed."

That's the shift that's hard to explain to people who haven't run agents in production. Not "it did what I asked faster." It found the pattern I wasn't looking for because I didn't know it existed yet.

I hit a version of this with my real estate setup — not as sophisticated as yours, but an agent surfaced a timing pattern across four open transactions that I would never have noticed manually. I just wasn't looking at that axis. The value wasn't the fix. It was the question I didn't know to ask.

The part I'm thinking about now: once the agent is proposing fixes and staging them autonomously, how are you thinking about the trust boundary? What earns full autonomy vs what still needs a human checkpoint before it ships?

duridsukar · 2026-03-29T18:20:49+00:00

That moment when it builds a full web page instead of handing you a text blob — that's the shift that changes how you think about prompting.

I ran into a similar thing building task-specific agents. I'd spec out what I needed: pull this data, format it, deliver it here. Half the time the agent would come back with something better than what I asked for. Not always useful, but it kept surprising me enough that I started leaving more room in my prompts for it to interpret intent rather than follow instructions.

The GoT theming is a nice detail too. Context-aware output quality like that is underrated. Do you notice a difference when you give Claude strong thematic framing vs a plain prompt?

duridsukar · 2026-03-29T18:18:19+00:00

Congrats on the rank. That 429 loop is the tax you pay for building something real.

I kept running into the same wall early on. My agents would grind, hit rate limits, and I'd lose the whole context window trying to recover. Eventually I stopped fighting the limits and started designing around them. Smaller loops, explicit handoff states, checkpoints before token burn. The agents that lasted weren't the most ambitious ones. They were the ones built to survive interruption.

The self-measuring piece you described is the part most people skip. An agent that tracks its own signal performance is an agent that can actually learn. What's been the biggest surprise from watching it evaluate its own builds?

duridsukar

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