CrewAI Proofreader

CREWAI EDITOR

Orchestrating a digital newsroom of expert agents to proofread at scale.

CrewAI Python Multi-Agent Systems Automation

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The Cost of Complexity

This wasn't a startup idea; it was a tuition fee. I built this to understand the "Hidden Taxes" of agentic workflows: latency, token costs, and orchestration fragility.

Orchestration Hell

Getting three LLMs to agree on a diff is harder than managing three human interns.

Token Burn

Learning CrewAI revealed how quickly context windows fill up when agents "talk" to each other.

Digital Newsroom

Elevator Pitch:
A CLI-based multi-agent system where specialized AI personas (Grammarian, Stylist, Fact-Checker) critique documents in parallel.

Approach	Pros	Cons	Verdict
Single LLM (ChatGPT)	Cheap & Fast.	Lacks nuance; prioritizes speed over rigor.	Baseline
Human Editor	High Agency.	Expensive ($50/hr); Slow turnover.	Too Slow
Agent Crew	Multi-perspective critique.	High Token Cost.	Selected

Specialized Agents

The Grammarian: Validates syntax.
The Stylist: Enforces brand voice.
The Manager: Compiles final diffs.

CrewAI Framework

Defining "Personas" to constrain LLM behavior.

Role: Defines the agent's expertise.

Goal: The specific outcome (e.g., "Find 5 errors").

Backstory: Prompt context to enforce persona (e.g., "You hate passive voice").

editor = Agent( role='Senior Editor', goal='Fix tone inconsistencies', backstory="""You are a veteran editor at wired.com. You prefer punchy, active sentences.""", verbose=True, allow_delegation=True ) task = Task( description=f"Critique this section: {text_chunk}", agent=editor )

The Hallucination Loop

Agent Conflict

"Without a strict Manager agent, the 'Stylist' would rewrite a sentence to be punchy, and the 'Grammarian' would rewrite it back to be formal, creating an infinite loop."

Solution: Implementing a hierarchical process where agents submit proposals, but only the Manager agent has write access to the final document.

The "Lesson"

Time Saved

~80%

Reduction in "first pass" editing time.

Token Cost

HIGH

Taught me the value of frugal prompting.

Outcome

Learned

Deep dive into CrewAI architecture.

Stack Details

Framework

CrewAI

Agent orchestration & task delegation.

LLM

GPT-4o

High reasoning capability for nuance.

Input

PyPDF2

Text extraction and chunking.

PART I: THE CONCEPT

Agents as Editors

Editing large technical documents is tedious. You need to check for grammar, consistency in tone, formatting standards, and technical accuracy. Doing this sequentially takes hours. I wanted to see if I could spin up a "team" of AI agents to do it in parallel.

Using the CrewAI framework, I built a system where different agents took on different personas:

The Grammarian: Focused solely on syntax and grammar.
The Stylist: Ensured the tone matched the target audience (e.g., "professional" vs "casual").
The Fact-Checker: Cross-referenced claims (limited to internal consistency).

PART II: IMPLEMENTATION

The Workflow

The system digests a PDF, splits it into chunks, and assigns these chunks to the agent crew. The agents critique the text and propose changes. A final "Manager Agent" reviews these proposals and compiles the final output.

# Defining the agents
editor = Agent(
  role='Senior Editor',
  goal='Ensure the document flows logically and is free of errors',
  backstory="""You are a veteran editor at a top tech publisher. 
  You hate passive voice and love clarity.""",
  verbose=True,
  allow_delegation=True
)

# Defining the task
proofread_task = Task(
  description=f"""Analyze the following text section: {text_chunk}""",
  agent=editor
)

PART III: LEARNINGS

Challenges & Learnings

Building this taught me a lot about Agent orchestration. The biggest challenge was "hallucination loops," where one agent would correct something, and another would correct it back. Strict prompt engineering and clear hierarchy (using the Manager agent) were essential to break these loops.

While effective for rough drafts, I found that for final polish, a human in the loop is still indispensable. However, this tool cut down the "first pass" editing time by about 80%.