Automating MiFID Compliance with CrewAI

	Regulatory context The MiFID II/III reviews introduce a single 7 % dark‑trading volume cap, ban payment for order flow (PFOF) and require Member States to ensure data‑quality standards for consolidated tape providers (legal.pwc.de). MiFID III mandates extended data fields and near‑real‑time reporting by September 2025 (asctechnologies.com). PFOF will be fully banned and a consolidated tape for equities, ETFs and derivatives will launch in June 2026 (asctechnologies.com). Key deadlines
	Member States must transpose the MiFID II/MiFIR review into national law by 28 September 2025 (legal.pwc.de). September 2025 is also the deadline for MiFID III reporting obligations (asctechnologies.com); the PFOF ban and consolidated tape come into force in June 2026 (asctechnologies.com). Challenges
	Firms face increased operational complexity: they must implement a single‑volume cap, monitor dark trading, ensure data quality for consolidated tapes and update policies and procedures (legal.pwc.de). Near‑real‑time reporting and expanded data fields demand modern IT infrastructure (asctechnologies.com). All customer conversations across channels must be recorded and analysed for misconduct, requiring AI‑powered risk detection (asctechnologies.com). Opportunities
	Greater transparency and harmonised data help investors and regulators. The EU‑wide consolidated tape offers real‑time market data (asctechnologies.com). AI and automation can transform compliance from a cost centre into a strategic differentiator: multi‑agent systems can handle large volumes of data, detect compliance risks and produce accessible reports. Automation approach
	CrewAI is a framework for building teams of autonomous agents that work together. A research agent gathers the latest MiFID updates using web‑search tools and summarises them; a writer agent turns this research into an engaging blog post. Tasks are executed sequentially, ensuring that the output of research feeds directly into writing.

Why MiFID Updates Matter

MiFID II was the EU’s flagship reform for securities markets. Seven years after its introduction, financial markets look very different: algorithmic trading, retail investing apps and AI‑assisted research have become mainstream. The European Commission therefore adopted a MiFID II/MiFIR review in February 2024. The review aims to boost transparency and investor protection by removing redundant provisions, creating a consolidated tape and banning PFOF (payment  for order flow) (legal.pwc.de). Member States have until 28 September 2025 to transpose the amendments into national law (legal.pwc.de).

In parallel, the so‑called MiFID III package—an informal term for amendments to MiFID II and MiFIR—modernises the rulebook to reflect digitalisation. It requires firms to document transactions in greater detail and report them to supervisory authorities in near real time (asctechnologies.com), introduces a single 7 % dark‑trading cap (asctechnologies.com), bans PFOF (asctechnologies.com) and launches a consolidated tape for equities, ETFs and derivatives (asctechnologies.com).

These changes present both challenges and opportunities for financial institutions. On the one hand, compliance will demand investments in data governance, monitoring tools and process redesign (legal.pwc.de). On the other hand, harmonised data and advanced analytics can enhance risk management and investor confidence. Adopting multi‑agent AI systems such as CrewAI can help firms navigate this landscape efficiently.

Building a Multi‑Agent MiFID Compliance Workflow

The traditional approach to regulatory change management involves human analysts reading lengthy legislative texts, summarising them and drafting internal or external communications. This process is time‑consuming and error‑prone. CrewAI offers an alternative: you can orchestrate multiple AI agents to collaborate on complex workflows.

Below is a high‑level example of how to automate MiFID research and content creation:

1. Research agent – A specialist agent queries real‑time web search tools (such as Serper) for the latest MiFID II/III developments. It extracts key facts—deadlines, major amendments, compliance obligations—and generates a structured report. The report highlights the 28 September 2025 deadline for transposition (legal.pwc.de), the September 2025 start of near‑real‑time reporting (asctechnologies.com), and the June 2026 PFOF ban and consolidated tape (asctechnologies.com).

2. Content strategist – This agent receives the research report and crafts a reader‑friendly article. It explains why the single‑volume cap and consolidated tape matter, discusses the pros and cons of the new regime, and includes practical advice for compliance teams. It also emphasises how AI and automation can help firms handle increased data volumes and monitoring requirements (asctechnologies.com).

3. Workflow orchestration – A Crew object ties these agents and tasks together in a sequential process. The research task must finish before the writing task begins, ensuring that the output is coherent.

Sample Implementation

Below is a simplified Python script (see crewai_mifid_example.py) that demonstrates this workflow. It uses the CrewAI framework and assumes you have set a valid Serper API key and configured a supported large language model.

import os
from crewai import Agent, Task, Crew, Process, LLM
from crewai_tools import SerperDevTool

# Set your Serper API key
os.environ['SERPER_API_KEY'] = 'your_serper_api_key'

# Configure your LLM
llm = LLM(
    model="gtp-4",
    max_tokens=2000,
)

# Define the research agent
research_agent = Agent(
    role="MiFID Research Analyst",
    goal="Investigate and summarize recent MiFID II and III updates",
    backstory="Expert in EU financial regulation",
    verbose=True,
    allow_delegation=False,
    llm=llm,
    tools=[SerperDevTool()],
)

# Define the writer agent
writer_agent = Agent(
    role="RegTech Content Strategist",
    goal="Write an engaging article about MiFID updates and AI compliance",
    backstory="Skilled communicator for tech audiences",
    verbose=True,
    allow_delegation=True,
    llm=llm,
)

# Define tasks
research_task = Task(
    description="Gather and analyse MiFID II/III deadlines, reporting requirements and PFOF ban.",
    agent=research_agent,
    expected_output="A structured report on MiFID updates",
)
writer_task = Task(
    description="Draft a 4‑paragraph blog post based on the research.",
    agent=writer_agent,
    expected_output="A blog post summarising MiFID updates and the role of AI in compliance.",
)

crew = Crew(
    agents=[research_agent, writer_agent],
    tasks=[research_task, writer_task],
    process=Process.sequential,
    verbose=True,
)

if __name__ == '__main__':
    result = crew.kickoff(inputs={'topic': 'MiFID II and MiFID III updates 2025'})
    print(result)

Pros and Cons of Using CrewAI for Regulatory Compliance

• Pros

  • Efficiency and scale: AI agents can monitor regulatory updates 24/7, quickly summarising amendments like the MiFID reviews. This reduces manual labour and speeds up response times.

  • Consistency: Automated pipelines deliver uniform analysis and writing quality, avoiding variability in human reporting.

  • Adaptability: Changing the topic (e.g., from MiFID to ESG or AI regulation) simply requires passing a different input string; no need to redesign the workflow.

  • Integrative power: Agents can be equipped with multiple tools—web search, document parsing, API connectors—allowing them to pull data from diverse sources.

• Cons

  • Context limitations: AI models may misinterpret nuanced legal language or miss implicit regulatory interactions. Human oversight remains essential.

  • Data‑source constraints: Tools like Serper rely on publicly available information. Some proprietary regulatory databases may be inaccessible.

  • Initial setup: Configuring secure LLM access and API keys requires careful attention to privacy and compliance policies.

  • Regulatory liability: Over‑reliance on automated interpretations could pose risks if the system outputs incorrect advice. Always validate AI‑generated content with legal experts.

Conda Environment

To reproduce the workflow locally, create a conda environment named crew with the following specification (see environment.yml):

name: crew
channels:
  - defaults
  - conda-forge
dependencies:
  - python=3.10
  - pip
  - pip:
      - crewai==0.193.2
      - crewai-tools==0.38.0
      - langchain==0.3.20
      - langchain-community==0.3.19
      - databricks-sdk==0.57.0
      - jupyter
      - ipykernel
      - openai

Create the environment with:

conda env create -f environment.yml
conda activate crew

Install any additional models or API keys (e.g., for Serper or your chosen LLM) as environment variables.

Final Thoughts

The MiFID II/MiFIR review and the forthcoming MiFID III rules represent the biggest shake‑up of EU securities regulation since 2018. Firms must prepare for tighter reporting timelines, a 7 % dark‑trading cap and the end of PFOF. At the same time, greater transparency and harmonised data open new opportunities for data‑driven trading strategies and improved investor protection.

Leveraging frameworks like CrewAI allows compliance teams to automate the tedious parts of regulatory change management. By delegating research and writing tasks to specialised agents, practitioners can focus on high‑level analysis and strategic decision‑making. As always, technology is a tool—not a substitute for legal judgement—but used wisely it can turn regulation into a competitive advantage.