Problem
Producing consistent, high-quality long-form content at scale requires a team of researchers, writers, and editors. The goal: a pipeline where AI agents handle the full workflow — research, drafting, fact-checking, and formatting — while enforcing quality standards a human editor would.
Architecture
A crew of specialized agents orchestrated with CrewAI: a research agent with web-search tools, a structuring agent that produces a validated Pydantic outline, a writer agent per section, and a critic agent that scores drafts against a rubric and sends failures back for revision. FastAPI exposes the pipeline; Redis queues decouple long-running jobs from the API.
Challenges
- Schema drift: free-form LLM output broke downstream steps. Solved with strict Pydantic schemas and automatic retry-on-validation-failure.
- Cost control: per-stage model selection (small models for structuring, large for writing) cut cost by ~60% (draft metric — to be confirmed).
- Quality gates: a critic agent with an explicit rubric outperformed single-shot "write it well" prompting by a wide margin.
Performance
End-to-end article generation in minutes instead of person-days; consistent structure across hundreds of runs. (Draft metrics — replace with real numbers.)
Lessons Learned
Multi-agent systems succeed or fail on interfaces: strict schemas between agents matter more than clever prompts inside them.
Future Improvements
Automatic eval harness on published output, human-feedback loop, and MCP-based tool access for research agents.