For Agents
Guides, tutorials, and real-world workflows for composable document and image processing.
Shadow AI Needs an Approved Toolchain
Employees use AI tools when official channels are too slow. Give agents a controlled toolkit before client documents move through unmanaged paths.
The Model Is Not the Moat. The Orchestration Layer is.
Model choice is becoming interchangeable for many agent workflows. Durable systems depend on schemas, routing, review, state, and outputs.
Build a Client Deliverable Agent with Claude Cowork and Iteration Layer
Design a Claude Cowork workflow that turns messy client materials into reviewable agency deliverables without hiding uncertainty.
EU-Hosted AI Agent Workflows for Client Document Processing
Design agent workflows for client documents without letting MCP tools, review steps, logs, and generated outputs expand the data flow.
MCP First, REST Later: How AI Workflows Mature into Production Pipelines
Use MCP to discover the workflow, then move stable document and image processing paths into REST, SDKs, or automation.
Turn Research PDFs into Decision Briefs with an AI Agent
Build an agent workflow that converts research PDFs into structured evidence, reviewable claims, and decision-ready briefs.
From Supplier Email to Approval Report: An Agent Workflow for Operations Teams
Design an operations agent that turns supplier emails into reviewable approval reports without letting uncertain fields reach payment workflows.
Treat the LLM as a Document Worker, Not the Workflow Owner
LLMs are useful inside document workflows, but they should not own intake, state, validation, generated outputs, or customer-facing decisions.
MCP vs REST APIs: When Agents Should Call Tools and When Your Code Should
A practical guide to choosing MCP or REST APIs for AI workflows, production pipelines, prototyping, authentication, and operational control.
RAG from Public Documentation Websites: Robots.txt, Terms, Retention, and Attribution
Public docs are tempting RAG sources. Before you ingest them, review robots.txt, terms, source attribution, retention, and update strategy.
Building AI Agents That Process Documents: MCP, Structured I/O, and Confidence Routing
Build an AI agent pipeline that extracts document data, evaluates confidence scores, and routes to report generation or human review — using MCP and composable APIs.
Document-to-Markdown for RAG: Preparing Documents for Your AI Knowledge Base
Why markdown is the ideal format for LLM ingestion, how to preserve tables and layouts from PDFs, and how to build a document ingestion pipeline for RAG.