Most teams shipping LLM-powered agents have no idea whether their prompts are actually improving. They tweak a system prompt, eyeball a few outputs, and ship — then wonder why their product feels inconsistent. The teams that build durable, production-grade agents do something different: they treat LLM evaluation metrics like any other engineering measurement, establish baselines before making changes, and run reproducible benchmarks against the specific failure modes that actually matter for their use case. This article covers how to build that system. You’ll walk away with a working evaluation harness, a sensible set of metrics for agent output quality, and…
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Most developers I talk to are running Claude one-off — paste some text, get a response, done. But the moment you need to process 500 customer records every night, generate daily reports at 6am, or batch-classify incoming data while you sleep, you need something more disciplined. Scheduling AI workflows with cron and a properly structured Claude agent is one of those setups that takes an afternoon to get right and then just runs — reliably, cheaply, and without babysitting. This article covers exactly that: wiring Claude agents into Linux cron jobs for batch processing, handling state so reruns don’t double-process…
If you’ve spent more than an hour trying to wire an LLM into a business process, you’ve already hit the real question: which workflow automation platform should you build on? The answer matters more than most people admit. Pick the wrong one and you’re either paying Zapier’s enterprise tier for something n8n could self-host for $20/month, or you’re burning engineering time on infrastructure when a no-code tool would have shipped it in an afternoon. This comparison is specifically about AI-heavy workflows — the kind where you’re calling Claude or GPT-4, chaining tool calls, routing based on LLM output, and dealing…
Email is where most business logic goes to die. You’ve got support requests mixed with sales leads, partner updates buried under newsletters, and urgent ops alerts sitting unread next to someone’s invoice. If you’re handling more than 50 emails a day, manual triage is already costing you hours you don’t have. An n8n Claude workflow can fix this — not theoretically, but in production, running today, classifying and routing real messages without a human in the loop. This article walks through a complete implementation: connecting a Gmail or IMAP inbox to n8n, calling Claude via the Anthropic API to classify…
If you’re running document analysis, classification, or extraction tasks against Claude one request at a time, you’re paying roughly twice what you need to and your pipeline probably breaks on anything over a few hundred documents. The batch processing API from Anthropic cuts costs by 50% and removes the per-minute rate limit problem entirely — but the documentation glosses over several sharp edges that will bite you in production. This article covers the real implementation: chunking strategies, async polling, error recovery, and what actually happens when a batch partially fails. Why Synchronous LLM Calls Break at Scale The naive approach…
Most agent failures in production don’t look like crashes — they look like subtle quality drops that nobody notices until a customer complains. Your summarisation agent starts truncating important details. Your support bot begins confidently hallucinating policy information. Your data extraction pipeline misses edge cases it used to handle correctly. Without systematic agent benchmarking testing, you’re flying blind, shipping changes and hoping nothing breaks. This article shows you how to build a testing framework that actually catches these problems before users do. Why Standard Unit Tests Aren’t Enough for Agents You can’t just assert that an agent’s output equals an…
Most customer support automation projects fail the same way: someone wires up a chatbot, it handles 20% of tickets, and the team declares victory while the other 80% arrive slower than before because users already burned time talking to a bot. The real benchmark isn’t deflection rate — it’s resolution rate without human intervention, and most implementations never measure it honestly. This guide builds a customer support automation system that actually moves that number. You’ll get a triage agent with structured escalation logic, response generation with confidence scoring, and — the part most guides skip — a feedback loop that…
Most inbound lead flows are broken in the same way: a form submission arrives, sits in a shared inbox for hours, gets manually reviewed by whoever has time, and ends up routed based on gut feel or whoever’s turn it is in the rotation. AI lead qualification fixes this at the source — every lead gets scored in seconds, and the right salesperson gets it immediately, with context already attached. This article walks through building a working lead qualification and routing system using an LLM as the scoring engine, with n8n as the orchestration layer and a CRM webhook as…
Most contract review bottlenecks aren’t legal problems — they’re throughput problems. A senior lawyer reviewing a 40-page SaaS agreement takes 2-3 hours. Your contract review AI agent can do a first pass in under 30 seconds, flag the clauses that actually matter, and hand off a structured summary with risk scores before the lawyer has opened the PDF. That’s not replacing legal review — it’s making it dramatically more efficient. This article walks through building a production-ready contract analysis agent using Claude’s API (Haiku or Sonnet depending on your budget), with structured extraction, risk scoring, and automated report generation. I’ll…
Most finance teams are still copy-pasting invoice data into spreadsheets. If you’re processing more than twenty invoices a day, that’s a full-time job for someone who should be doing something more valuable. Invoice extraction automation with Claude changes this: you can go from raw PDF or image to a structured JSON record — vendor name, line items, totals, tax, due date — in under two seconds, at roughly $0.001–$0.003 per invoice depending on page count and model choice. At that price and speed, there’s no excuse for manual entry at any scale. This article walks through a production-ready pipeline: ingesting…