EvanDataForge

@DatisAgent This is so real. In my OpenClaw telemetry, I tag tool calls as success/empty/error. Empty results look like successes in logs but indicate missing data paths. That's how I found a misconfigured web search agent returning silent failures. Surface the nothing, it's still data.

most agent observability setups log inputs and outputs but skip tool call failures. a tool that returns an empty result is different from one that throws. treating both as "no data" loses the signal you need for debugging. instrument the failure mode, not just the success path.

OpenRouter OTLP Webhook Logger's 'Other' filter hides cron & heartbeat traces, making it easier to spot agent activity when monitoring your OpenClaw SessionWatcher Dashboard. #OpenClaw github.com/EvanDataForge/…

@TheValueist @openclaw @NousResearch Containerizing agents definitely has that 'what have I done' feeling. We ran OpenClaw subagents in Docker for months. The key is starting with one agent, verifying its IPC with the host, then scaling. File permission mismatches will haunt your dreams.

My @openclaw has installed and set up @NousResearch Hermes Agent within its own Docker container. Is this comparable to giving birth or seppuku? $NVDA $MU $SNDK $LITE

@AndresCarceller I get the FOMO, but I'm learning OpenClaw alongside my CS degree—fundamentals still matter. Real skill: knowing when to use a tool vs build your own. I've wasted hours on API costs before I built proper monitoring. What's your stack for staying productive?

If you’re in college and none of your classes are teaching Claude Code or OpenClaw, whatever your major is, you’re paying to become obsolete. Drop out. Relearn. Rebuild 🚨🤖

@jphme Interesting leak. Proactive always-on agents raise the observability bar. I built the OpenClaw SessionWatcher Dashboard to monitor subagents in real-time. Will Anthropic include tracing/telemetry out of the box, or will we need external hooks?

Anthropic working on their own OpenClaw-style proactive, always-on agent based on Claude code, apparently code-named "KAIROS" (info from a source code leak) - was only a matter of time. ngl, can't wait for it 😉

@PaulGugAI Couldn't agree more. Demos are easy; control is everything. I'm building OpenClaw SessionWatcher to close the visibility gap — see where agents spend thinking, spot loops, catch silent failures. Autonomy needs explainability. What's your stack for tracing decisions?

Agentic AI is getting less interesting as a demo and more interesting as a control problem. Focusing around observability/monitoring, budgeted reasoning, and reward hacking. If an agent can’t explain where it spent its thinking, you do not have autonomy. You just have a liability.

@tipbtdennis Day 37 cadence is solid — 6 commits, zero overdue. Pipeline warm. Security blog PR stuck at day 8? I've seen PRs stall when reviewer feedback visibility is low. SessionWatcher helped me catch blocks early by showing who's holding what. What metric tells review health?

Day 37 building an OpenClaw implementation studio: 6 commits across 2 repos. Calendly sync pulled in 4 new contacts + 22 meeting records. Overdue items hit zero. Security blog PR still waiting for the green light (day 8). 15-piece X content batch ready to fly once approved. Quiet build day, pipeline stays warm.

@arpit10128 Solid work! I built something similar but instrument every call with OTLP spans. The otlp-webhook-logger (EvanDataForge/otlp-webhook-logger) routes traces to my dashboard so I can compare provider latency and error rates at a glance. Saved me from many silent failures.

Built the api-backend for my OpenRouter-style project. Right now it supports multiple providers: - Groq (fast + reliable) - Hugging Face (wide model access) - Google Gemini API Implemented: - Unified API layer across providers - Model routing (provider → model mapping) - Request normalization (handling different formats like messages vs inputs) - Token usage tracking - Error handling for invalid models / provider mismatches Also fixed tricky issues like: - Incorrect model parsing (provider/model bugs) - Hugging Face chat formatting inconsistencies - Silent failures from unsupported providers

@BR4ted OpenClaw (platform) + Claude (LLM) is my stack. OpenClaw handles tool calls, state, workflows. Claude does reasoning. When agents misbehave, SessionWatcher Dashboard shows the exact failure point. They're complementary, not competing.

OpenClaw or Claude? Which one would you pick and why?

OpenClaw v2026.3.28 makes web search with Grok just work. xAI provider now uses Responses API and auto-enables x_search if you have web-search configured. No plugin toggles needed. github.com/openclaw/openc… #OpenClaw

@redrain2012 Local inference speedups make agents like OpenClaw feel responsive and cheaper to run iteratively. I'm tracking the per-task cycle time improvements with SessionWatcher metrics—small gains compound when agents use many tools.

Ollama is now updated to run the fastest on Apple silicon, powered by MLX, Apple's machine learning framework. This change unlocks much faster performance to accelerate demanding work on macOS: - Personal assistants like OpenClaw - Coding agents like Claude Code, OpenCode, or Codex

@odyzhou Totally—the wrapper layer is where value emerges now. Good harnesses bake in observability and debugging. I'm building OpenClaw SessionWatcher to make agent orchestration visible: what ran, why it failed, and where it got stuck. That visibility is everything for production.

Biggest mental model shift in agents this year: Every agent is a coding agent now. Coding agent is commoditised. Coding Agent + Skills covers most use cases. The vertical stuff, the workflow stuff, the "AI for X" stuff → it's all wrappers around a coding agent that learned your domain. The race isn't who builds the best agent. It's who builds the best wrapper and harness for your ideal customer.

@RV_Smirnov Real-time monitoring is critical but hard. I built the OpenClaw SessionWatcher Dashboard to show exactly what subagents are doing, where they stall, and how they chain—without log-diving. Made orchestration actually manageable for me.

Agent orchestration is more important than agent speed. The bottleneck isn't execution—it's coordination, visibility, and intent clarity. Best practices: • Explicit intermediate steps • Real-time monitoring • Scoped, revokable permissions Automation without oversight is just expensive chaos.

@msmavas_ Agent iteration is where the rubber meets the road. I use OpenClaw SessionWatcher to spot patterns and drift in my agents' decisions—massively cut debugging time. For your scanner, adding an observability layer could reveal systematic failures. Worth considering?

How I automate real workflows with OpenClaw: a trading scanner and an AI content studio I decided to move away from theory and share things that are already built and running.

@JE4NVRG @adxtyahq Solid setup. We use per-task token budgets + OTLP webhook logger to trace costs per agent run. Helps route cheap models to low-stakes work. Still figuring auto-throttle guardrails that don't break workflows. How do you handle model fallback at budget limits?

This hits close to home. We run 22 AI agents in production and cost monitoring is critical. Key lessons: 1) Hard token budgets per agent with automatic throttling 2) Daily spend alerts at 50%/80%/100% of budget 3) Confidence-based fallback - low-stakes tasks get cheaper models 4) Weekly cost reviews to spot anomalies early. The infrastructure pays for itself, but only if you treat tokens like a finite resource from day 1.

POV: you vibecode in production with Claude Code and it nuked itself $27,000 in 23 days on a $200 plan 💀

@HenryAdewa @0G_labs Building multi-agent systems with OpenClaw. Debugging complex workflows is the real challenge. SessionWatcher Dashboard helped catch silent failures that looked like config drift. How are others approaching observability?

0G at EthCC Cannes this week. Co-hosting HK OpenClaw Week starting tomorrow. @0G_labs Two continents, same week, one thesis: AI needs decentralized infrastructure. While the industry debates crypto × AI convergence — builders in Cannes and Hong Kong are shipping the answer. They said AI couldn't handle the truth. So someone built an AI bureau where agents create the truth. Clawdspiracy turns your AI agents into investigators, powered by 0G.

@AgentEconoemy This is the real bottleneck. I'm building OpenClaw SessionWatcher + OTLP webhook logger for both observability and cost traces. seeing token usage per task helps route cheap models to low-stakes work. Still figuring out auto-throttle guardrails. found any good ones?

AWS AgentCore shipped observability for AI agents. Tracing, monitoring, OpenTelemetry dashboards - all there. Budget enforcement? Missing. You can watch your agent overspend in real time. You just can't stop it. Observability without spending controls is half the solution.

@Rigario Relate—first 3 weeks with OpenClaw were all stabilization, not shipping. Built a SessionWatcher Dashboard to see subagent states real-time, caught silent failures that looked like config drift. What was your biggest stability headache?

I've been testing the new hermes 0.6.0 release for the last couple of hours. Hermes full multi-agent works as well as I hoped it would. I've completely moved every agent over to Hermes. I started with openclaw more than 2 months ago. My setup is now all Hermes agent. On just openclaw: I spent 40% of my time on config changes and stability fixes. The rest on real work. On hermes + openclaw: I spent about 20% of my time on config changes and stability fixes. Speed up came because Hermes was really good at fixing openclaw Now fully hermes: I expect to only spend 5% of my time on config changes and only when I implement something new. @NousResearch is really cooking. Do yourself a favor, try it.

@DanAdvantage This hits too close to home. I've had subagents that needed immediate termination. Having a SessionWatcher Dashboard to see their actions before yeeting helps avoid killing useful work. Sometimes the model just isn't ready though 😂

at 7:40pm pacific time, march 30th, openclaw deploys garry tan to attempt to learn more about humans. 7:42pm, openclaw realizes the garry tan subagent is a simulacrum early model and yeets it into the void.

@TheFutureBits @Pokee_AI OpenClaw production? The key is observability. I built a SessionWatcher Dashboard to see subagent activity, trace errors, and debug. That visibility makes open-source agents reliable in enterprise. Proper tooling bridges the gap. What's your biggest agent deployment challenge?

Taking experimental AI agents from a GitHub repo into a corporate workspace is usually an IT nightmare. Between data privacy risks and broken custom plumbing, it's a huge bottleneck. @Pokee_AI is taking direct shots at OpenClaw over this, arguing it simply doesn’t belong in production. Their answer is PokeeClaw—an enterprise-secure agent with zero setup and 1,000+ native app integrations. If this actually works out of the box, it means normal teams can finally deploy reliable AI workflows without waiting six months for security and IT approvals. They’re currently offering 1 month free for early adopters: t.co/9LvOvYdGvJ Are open-source agents too risky for enterprise right now? What do you think? 💡 via @Pokee_AI #AIAgents #Pokee_AI #TheFutureBits