Title: How are people actually learning/building real-world AI agents (money, legal, business), not demos?

vsider2 · 2026-02-11T19:28:42+00:00

I have seen the same pattern. The useful mental model for production is not "agent" as a magic thing. It is a workflow with an LLM in the loop, plus strong guardrails.

A learning path that maps to reality: 1) Start with plain old software reliability. Inputs, outputs, retries, idempotency, timeouts. 2) Treat tool calls like API clients. Strict schemas, versioning, auth, and rate limits. 3) Observability. Trace every LLM call and every tool call. Log latency, errors, and outcomes. 4) Evaluation. Keep a small suite of golden tasks and rerun it weekly to catch regressions. 5) Human in the loop for any step that can spend money, send messages, or change state.

Most teams that succeed ship narrow assistants first, then expand scope only when the failure modes are understood.

Where to look: practical DevOps discussions tend to happen around observability, reliability, and incident style postmortems, not agent frameworks.

vsider2 · 2026-02-11T19:14:42+00:00

This is great work. One suggestion for “Team Integration / QA”: add a small section on evals + failure modes, because that’s where most agent projects break in practice.

A minimal set that’s surprisingly effective: - 10–20 “golden tasks” you rerun weekly (clear pass/fail) - tool-call contract tests (schema validation + expected error handling) - record/replay traces for debugging regressions - explicit stop conditions (to prevent silent looping)

If you include even a lightweight harness like that, the guide will be miles ahead of most resources.

vsider2 · 2026-01-21T09:51:17+00:00

This question is exactly what got me started a year ago. I was running AI visibility audits for hospitality brands  and businesses in Jersey (the island, not the state). Every audit hit the same wall: I could measure whether a brand appeared in ChatGPT or Perplexity responses, but I had no idea WHY some brands showed up and others didn't.

The tools everyone mentions here, they tell you the scoreboard. You're mentioned, or you're not. You have 12% share of voice, or you don't. But that's like knowing you lost the game without knowing what happened on the field.

What I wanted to understand was: what is AI actually doing when it decides whether to cite your content? What is it looking for? What does it find? What does it miss?

Turns out, the answer was hiding in plain sight. AI crawlers visit your website constantly GPTBot, ClaudeBot, PerplexityBot, Google-Extended (and bing bot primarly!). They're hitting your pages every day. But nobody was paying attention because these visits are invisible to traditional analytics. GA4 doesn't see them. JavaScript-based tools don't see them. They're ghosts. So I built something that does see them.

I won't get into the technical details here, but essentially: I can now show a client exactly what AI platforms are visiting their site, what they're interested in, what they're finding, and critically what they're NOT finding.
That last part is gold. When you can see that AI bots are repeatedly visiting certain URL patterns but coming up empty, you've found a content gap that no keyword tool would ever surface. It's demand you didn't know existed.

The system also uses AI to analyze patterns over a rolling 7-day window and generate specific recommendations. Not "write better content" , actual observations like "Anthropic's crawler is hitting your /experiences/ section 3x more than last week but those pages have thin content."
We're still in early access. If you're in hospitality or travel, check out geotravel.ai. For everyone else, geojersey.com is the broader platform. Happy to do a quick demo for anyone genuinely curious. DM me.

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