A simple framework for AI agent pilot projects
The point of AI agents is that they can actually go out and perform tasks on our behalf, rather than just process data and respond.
So, OpenAI’s Operator, which interacts directly with websites, can perform tasks like finding the best online deals for a given item and actually completing orders. Meanwhile Manus, the new Chinese agentic tech which is creating big waves, appears to be even more capable, handling more complex, cross-platform workflows.
A client is considering a similar concept for social media prospecting, scouting, or any process that ordinarily requires hours of manual research. It's an evolution beyond the typical question-and-answer or content-generation mode of GenAI: the AI can actually do things in the real world.
The issues we are tackling are certainly not unique to this project, so we agreed I could share them here. And of course, we’d love your feedback and suggestions, too.
Governance Challenges: Data, APIs, and Compliance
From a governance perspective, we have to figure out how these agents are accessing data, which APIs they’re using, and we have to ensure the data flow remains compliant.
Also from an ethical standpoint, we need clarity on how they’re interacting with websites or third-party platforms. This raises interesting issues like transparency: should the agent identify itself as a bot or an AI assistant? There will be privacy concerns as well.
My client has been experimenting with a Python-based “prospecting” tool. Essentially, it scans social platforms (within the limits of what their APIs permit) and analyzes user content. Then it provides a dashboard for filtering the results.
I would not yet call it an autonomous “agent” that completes tasks, but you can see how easily it could be extended to do so. For instance, the user might say, Find all potential leads who are discussing a new product line, and schedule a meeting with them. If scheduling APIs are integrated, this becomes pretty straightforward.
That does present some interesting governance challenges. For instance, how do we label that data, store it, and ensure it’s used in accordance with platform policies and data privacy laws? If this involves consumer data from social media, we definitely need to watch out for personal identifiable information (PII) usage.
That’s precisely my concern. Tools that simulate or stand in for a human can run afoul of privacy policies or regulations. And of course we also need to consider the user’s consent.
For now, we’re working within the official APIs, which typically provide user permissions up to a limit. We can maintain compliance, but only if we carefully handle tokens and data.
I think the bigger challenge is that autonomous agents can be difficult to keep within strict boundaries. And we all know that the AI is not perfect. We still need a human in the loop for critical decisions. But for triaging large data sets or offering initial recommendations, AI agents will be amazing.
So, we need robust checks and balances. If an agent is scanning the web to find prospective leads, how do we confirm it’s collecting accurate info and not mixing data from multiple sources or misrepresenting them?
We could implement data lineage and traceability. Every dataset or marketing lead that the AI identifies should be tied back to a verifiable data source. That’s how we ensure we know where the data came from, how it was processed, and how confident we are in its reliability.
Ethical Dilemmas: Transparency and Consent
From an ethical standpoint, we may also consider explaining to stakeholders (both internal and external) how these agents operate. If someone receives an automated response or booking confirmation, they need to know that it was an AI. Transparency enables customer trust.
Another angle is ensuring we stay in compliance with different platform rules, because each social media site has its own policy around scraping or automated data collection. Rate limits are different and special permissions are nearly always required.
So one thing we need to work on is a robust compliance checklist that covers platform permissions and (in this case) GDPR requirements for user data if it’s personal, and guidelines for data retention.
On top of that, these agents, once fully operational, will likely integrate with other corporate systems. That raises further governance questions, like what if the AI tries to place an order using outdated credentials or we end up with an unauthorized transaction?
And it’s not just about external rules: the client has their own corporate ethics guidelines. If they're using an AI agent to message potential leads, how do they ensure we’re not inadvertently spamming or misrepresenting their intentions?
A Step-by-Step Pilot Framework for AI Agents
For the data science team, I proposed a pilot of a carefully scoped project. For instance, they will limit the agent’s capabilities to read-only tasks at first: like scanning publicly available posts and summarizing them. Then, we’ll add more advanced features, like sending messages, only after we set up adequate oversight.
We want to start small: define success metrics, success thresholds, and have a plan for scaling. Then we have to bring in other teams.
The data governance folks to make sure the data catalog is updated. Also, we will need new fields or meta-tags for “AI-collected data.”
Legal counsel to ensure there Are disclaimers and compliance language in place. If they roll this out and it interacts with external platforms, they have to confirm that it’s consistent with existing public-facing policies.
The technical scope for the pilot will include straightforward demonstration, like scanning posts related to an upcoming product launch, then creating a prioritized contact list. That can be treated as a closed test environment so we can measure how well it works before we let it handle anything critical.
At this point, I think we have a plan in place that may be usefully generic for anyone planning a pilot with agentic AI ...
1. Pilot Phase – Start small, read-only tasks, strictly governed environment.
2. Compliance and Oversight – Ensure platform terms of service are respected, data is traceable, and disclaimers are provided.
3. Data Catalog Updates – Create metadata for AI-collected insights so we know their source and reliability.
4. Ethical/Legal Review – Provide transparency in user interactions and remain consistent with corporate and regulatory guidelines.
5. Document – Precisely how the agent is used, what data it accesses, and how that data is processed.
6. Human Oversight – You can’t rely on the agent alone. Human experts must verify critical outputs to avoid compliance or PR issues.
Here it is laid out diagramatically …
