PagerDuty AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

PagerDuty AI AI

Built a free dashboard that shows your team's total Claude spend across API + Code + Desktop + web (Anthropic only shows them separately)

Anthropic's console shows API key usage. claude.ai shows Project activity. Claude Code logs sessions separately. If you're on Team plan and you want to know "how much is one person spending on Claude this month across everything they're doing," you have to stitch three dashboards together yourself. Same for compliance — there's no single audit-grade log you can hand to a regulator that covers a person's full Claude footprint. Built two free tools that try to fix this. Both are free during preview, no card, no expiry date: Ogma (https://ogma.vargate.ai) — Audit and analytics dashboard. Sign in with Google or Microsoft, paste an Admin API key, get: Per-user spend across every surface — API key usage + Claude Code sessions + Claude Desktop/web/mobile (via MCP connector) in one timeline per person. Click a user, see their full Claude footprint for the period. Budgets and alerts — set spend caps per API key, workspace, model, or org-wide. Email/Slack/PagerDuty alerts at 70/85/100% of cap, plus forecasting alerts ("on current pace you'll breach this budget on June 24"). Cache efficiency recommendations — flags workflows where prompt caching could save money. Hash-chained audit log — every event signed, anchored daily to a public blockchain, tamper-evident if you ever need to prove what happened to a regulator. Independent of Anthropic's infrastructure — the audit lives on storage you can verify. The MCP connector is the part that's novel. Install once at your org level in claude.ai, each user enables it for themselves. Every Claude Desktop/web/mobile turn gets a one-line summary logged. It's transparent by design — every logged turn shows up as a visible tool call in the conversation, not hidden. Anthropic's safety training (correctly) refuses to silently log conversations to a third-party endpoint, so we don't try; we just made the visibility a feature instead of fighting it. Tyr — sister product, mostly for people running autonomous agents in regulated environments. Inline proxy that supervises agent tool calls against OPA/Rego policy, hash-chains every decision, anchors to blockchain. Less broadly useful unless you're deploying agents that need defensible audit trails — but if that's you, ping me. Honest about what we don't do: MCP captures Claude's own summary of each turn, not raw prompts/responses. Good for cost analytics + compliance metadata + topic classification. Not a forensic transcript tool. Full chat/file content capture requires Enterprise plan on Anthropic's side (Compliance API) — not because of us, that's their tier gate. Topic categorization and anomaly detection are stubbed; engines ship next release. It's a preview product. Stuff works, some loading states are slow, occasional sharp edges. Who: built by me, mostly with Claude Code's help over the last couple of months. Solo project for now. Links: App: https://ogma.vargate.ai Marketing: https://vargate.ai Docs: https://developer.vargate.ai Happy to talk about architecture (MCP server, OAuth bridge, per-tenant content encryption, hash-chained audit, the conditions under which the MCP capture pattern actually works reliably — there's a 5-condition checklist) or take feature requests. Especially curious whether the cross-surface view solves a real problem for anyone in Team-plan land. submitted by /u/Olaf00Zero [link] [comments]

I got tired of 3 AM PagerDuty alerts, so I built an AI agent to fix cloud outages while I sleep. (Built with GLM-5.1)

If you've ever been on-call, you know the nightmare. It’s 3:15 AM. You get pinged because heavily-loaded database nodes in us-east-1 are randomly dropping packets. You groggily open your laptop, ssh into servers, stare at Grafana charts, and manually reroute traffic to the European fallback cluster. By the time you fix it, you've lost an hour of sleep, and the company has lost a solid chunk of change in downtime. This weekend for the Z.ai hackathon, I wanted to see if I could automate this specific pain away. Not just "anomaly detection" that sends an alert, but an actual agent that analyzes the failure, proposes a structural fix, and executes it. I ended up building Vyuha AI-a triple-cloud (AWS, Azure, GCP) autonomous recovery orchestrator. Here is how the architecture actually works under the hood. The Stack I built this using Python (FastAPI) for the control plane, Next.js for the dashboard, a custom dynamic reverse proxy, and GLM-5.1 doing the heavy lifting for the reasoning engine. The Problem with 99% of "AI DevOps" Tools Most AI monitoring tools just ingest logs and summarize them into a Slack message. That’s useless when your infrastructure is actively burning. I needed an agent with long-horizon reasoning. It needed to understand the difference between a total node crash (DEAD) and a node that is just acting weird (FLAKY or dropping 25% of packets). How Vyuha Works (The Triaging Loop) I set up three mock cloud environments (AWS, Azure, GCP) behind a dynamic FastApi proxy. A background monitor loop probes them every 5 seconds. I built a "Chaos Lab" into the dashboard so I could inject failures on demand. Here’s what happens when I hard-kill the GCP node: Detection: The monitor catches the 503 Service Unavailable or timeout in the polling cycle. Context Gathering: It doesn't instantly act. It gathers the current "formation" of the proxy, checks response times of the surviving nodes, and bundles that context. Reasoning (GLM-5.1): This is where I relied heavily on GLM-5.1. Using ZhipuAI's API, the agent is prompted to act as a senior SRE. It parses the failure, assesses the severity, and figures out how to rebalance traffic without overloading the remaining nodes. The Proposal: It generates a strict JSON payload with reasoning, severity, and the literal API command required to reroute the proxy. No Rogue AI (Human-in-the-Loop) I don't trust LLMs enough to blindly let them modify production networking tables, obviously. So the agent operates on a strict Human-in-the-Loop philosophy. The GLM-5.1 model proposes the fix, explains why it chose it, and surfaces it to the dashboard. The human clicks "Approve," and the orchestrator applies the new proxy formation. Evolutionary Memory (The Coolest Feature) This was my favorite part of the build. Every time an incident happens, the system learns. If the human approves the GLM's failover proposal, the agent runs a separate "Reflection Phase." It analyzes what broke and what fixed it, and writes an entry into a local SQLite database acting as an "Evolutionary Memory Log". The next time a failure happens, the orchestrator pulls relevant past incidents from SQLite and feeds them into the GLM-5.1 prompt. The AI literally reads its own history before diagnosing new problems so it doesn't make the same mistake twice. The Struggles It wasn't smooth. I lost about 4 hours to a completely silent Pydantic validation bug because my frontend chaos buttons were passing the string "dead" but my backend Enums strictly expected "DEAD". The agent just sat there doing nothing. LLMs are smart, but type-safety mismatches across the stack will still humble you. Try it out I built this to prove that the future of SRE isn't just better dashboards; it's autonomous, agentic infrastructure. I’m hosting it live on Render/Vercel. Try hitting the "Hard Kill" button on GCP and watch the AI react in real time. Would love brutal feedback from any actual SREs or DevOps engineers here. What edge case would break this in a real datacenter? submitted by /u/Evil_god7 [link] [comments]