How do you implement an AI agent for intelligent document generation?

Document generation agents create structured documents (contracts, proposals, reports, presentations) from natural language instructions. Architecture: template library (parameterized document templates for each document type), content generator (LLM-based section-by-section generation with style guidelines), data integrator (pulls relevant data from CRM, databases, and other sources), and formatter (converts generated content to final format: PDF, DOCX, PPTX). Workflow: user specifies document type and key parameters, agent retrieves a template and relevant data, generates content section by section with appropriate tone and formatting, applies brand guidelines (fonts, colors, headers), and produces the final document. Implementation: use a combination of template engines (Handlebars, Jinja2) for structured sections and LLM generation for dynamic content. For contracts: retrieve applicable clauses from a clause library, customize based on deal parameters, and flag non-standard terms for legal review. For proposals: pull client information from CRM, include relevant case studies, generate customized pricing tables, and create an executive summary. Quality controls: implement style checking (grammar, tone consistency), fact verification against source data, compliance checking for regulated documents, and human review workflow for high-stakes documents. Output: support multiple formats through libraries like python-docx, reportlab (PDF), and python-pptx, with consistent branding applied automatically.