In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss managing AI agent teams with Project Management 101. You will learn how to translate scope, timeline, and budget into the world of autonomous AI agents. You will discover how the 5P framework helps you craft prompts that keep agents focused and cost‑effective. You will see how to balance human oversight with agent autonomy to prevent token overrun and project drift. You will gain practical steps for building a lean team of virtual specialists without over‑engineering. Watch the episode to see these strategies in action and start managing AI teams like a pro.

Watch the video here:

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

[podcastsponsor]

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn: In this week’s In‑Ear Insights, one of the big changes announced very recently in Claude code—by the way, if you have not seen our Claude series on the Trust Insights live stream, you can find it at trustinsights.

Christopher S. Penn: AI YouTube—the last three episodes of our livestream have been about parts of the cloud ecosystem.

Christopher S. Penn: They made a big change—what was it?

Christopher S. Penn: Thursday, February 5, along with a new Opus model, which is fine.

Christopher S. Penn: This thing called agent teams.

Christopher S. Penn: And what agent teams do is, with a plain‑language prompt, you essentially commission a team of virtual employees that go off, do things, act autonomously, communicate with each other, and then come back with a finished work product.

Christopher S. Penn: Which means that AI is now—I’m going to call it agent teams generally—because it will not be long before Google, OpenAI and everyone else say, “We need to do that in our product or we’ll fall behind.”

Christopher S. Penn: But this changes our skills—from person prompting to, “I have to start thinking like a manager, like a project manager,” if I want this agent team to succeed and not spin its wheels or burn up all of my token credits.

Christopher S. Penn: So Katie, because you are a far better manager in general—and a project manager in particular—I figured today we would talk about what Project Management 101 looks like through the lens of someone managing a team of AI agents.

Christopher S. Penn: So some things—whether I need to check in with my teammates—are off the table.

Christopher S. Penn: Right.

Christopher S. Penn: We don’t have to worry about someone having a five‑hour breakdown in the conference room about the use of an Oxford comma.

Katie Robbert: Thank goodness.

Christopher S. Penn: But some other things—good communication, clarity, good planning—are more important than ever.

Christopher S. Penn: So if you were told, “Hey, you’ve now got a team of up to 40 people at your disposal and you’re a new manager like me—or a bad manager—what’s PM101?”

Christopher S. Penn: What’s PM101?

Katie Robbert: Scope, timeline, budget.

Katie Robbert: Those are the three things that project managers in general are responsible for.

Katie Robbert: Scope—what are you doing?

Katie Robbert: What are you not doing?

Katie Robbert: Timeline—how long is it going to take?

Katie Robbert: Budget—what’s it going to cost?

Katie Robbert: Those are the three tenets of Project Management 101.

Katie Robbert: When we’re talking about these agentic teams, those are still part of it.

Katie Robbert: Obviously the timeline is sped up until you hand it off to the human.

Katie Robbert: So let me take a step back and break these apart.

Katie Robbert: Scope is what you’re doing, what you’re not doing.

Katie Robbert: You still have to define that.

Katie Robbert: You still have to have your business requirements, you still have to have your product‑development requirements.

Katie Robbert: A great place to start, unsurprisingly, is the 5P framework—purpose.

Katie Robbert: What are you doing?

Katie Robbert: What is the question you’re trying to answer?

Katie Robbert: What’s the problem you’re trying to solve?

Katie Robbert: People—who is the audience internally and externally?

Katie Robbert: Who’s involved in this case?

Katie Robbert: Which agents do you want to use?

Katie Robbert: What are the different disciplines?

Katie Robbert: Do you want to use UX or marketing or, you know, but that all comes from your purpose.

Katie Robbert: What are you doing in the first place?

Katie Robbert: Process.

Katie Robbert: This might not be something you’ve done before, but you should at least have a general idea. First, I should probably have my requirements done. Next, I should probably choose my team.

Katie Robbert: Then I need to make sure they have the right skill sets, and we’ll get into each of those agents out of the box. Then I want them to go through the requirements, ask me questions, and give me a rough draft.

Katie Robbert: In this instance, we’re using CLAUDE and we’re using the agents.

Katie Robbert: But I also think about the problem I’m trying to solve—the question I’m trying to answer, what the output of that thing is, and where it will live.

Katie Robbert: Is it just going to be a document? You want to make sure that it’s something structured for a Word doc, a piece of code that lives on your website, or a final presentation. So that’s your platform—in addition to Claude, what else?

Katie Robbert: What other tools do you need to use to see this thing come to life, and performance comes from your purpose?

Katie Robbert: What is the problem we’re trying to solve? Did we solve the problem?

Katie Robbert: How do we measure success?

Katie Robbert: When you’re starting to…

Katie Robbert: If you’re a new manager, that’s a great place to start—to at least get yourself organized about what you’re trying to do. That helps define your scope and your budget.

Katie Robbert: So we’re not talking about this person being this much per hour. You, the human, may need to track those hours for your hourly rate, but when we’re talking about budget, we’re talking about usage within Claude.

Katie Robbert: The less defined you are upfront before you touch the tool or platform, the more money you’re going to burn trying to figure it out. That’s how budget transforms in this instance—phase one of the budget.

Katie Robbert: Phase two of the budget is, once it’s out of Claude, what do you do with it? Who needs to polish it up, use it, etc.? Those are the phase‑two and phase‑three roadmap items.

Katie Robbert: And then your timeline.

Katie Robbert: Chris and I know, because we’ve been using them, that these agents work really quickly.

Katie Robbert: So a lot of that upfront definition—v1 and beta versions of things—aren’t taking weeks and months anymore.

Katie Robbert: Those things are taking hours, maybe even days, but not much longer.

Katie Robbert: So your timeline is drastically shortened. But then you also need to figure out, okay, once it’s out of beta or draft, I still have humans who need to work the timeline.

Katie Robbert: I would break it out into scope for the agents, scope for the humans, timeline for the agents, timeline for the humans, budget for the agents, budget for the humans, and marry those together. That becomes your entire ecosystem of project management.

Katie Robbert: Specificity is key.

Christopher S. Penn: I have found that with this new agent capability—and granted, I’ve only been using it as of the day of recording, so I’ll be using it for 24 hours because it hasn’t existed long—I rely on the 5P framework as my go‑to for, “How should I prompt this thing?”

Christopher S. Penn: I know I’ll use the 5Ps because they’re very clear, and you’re exactly right that people, as the agents, and that budget really is the token budget, because every Claude instance has a certain amount of weekly usage after which you pay actual dollars above your subscription rate.

Christopher S. Penn: So that really does matter.

Christopher S. Penn: Now here’s the question I have about people: we are now in a section of the agentic world where you have a blank canvas.

Christopher S. Penn: You could commission a project with up to a hundred agents. How do you, as a new manager, avoid what I call Avid syndrome?

Christopher S. Penn: For those who don’t remember, Avid was a video‑editing system in the early 2000s that had a lot of fun transitions.

Christopher S. Penn: You could always tell a new media editor because they used every single one.

Katie Robbert: Star, wipe and star.

Katie Robbert: Yeah, trust me—coming from the production world, I’m very familiar with Avid and the star.

Christopher S. Penn: Exactly.

Christopher S. Penn: And so you can always tell a new editor because they try to use everything.

Christopher S. Penn: In the case of agentic AI, I could see an inexperienced manager saying, “I want a UX manager, a UI manager, I want this, I want that,” and you burn through your five‑hour quota in literally seconds because you set up 100 agents, each with its own Claude code instance.

Christopher S. Penn: So you have 100 versions of this thing running at the same time. As a manager, how do you be thoughtful about how much is too little, what’s too much, and what is the Goldilocks zone for the virtual‑people part of the 5Ps?

Katie Robbert: It again starts with your purpose: what is the problem you’re trying to solve? If you can clearly define your purpose—

Katie Robbert: The way I would approach this—and the way I recommend anyone approach it—is to forget the agents for a minute, just forget that they exist, because you’ll get bogged down with “Oh, I can do this” and all the shiny features.

Katie Robbert: Forget it. Just put it out of your mind for a second.

Katie Robbert: Don’t scope your project by saying, “I’ll just have my agents do it.” Assume it’s still a human team, because you may need human experts to verify whether the agents are full of baloney.

Katie Robbert: So what I would recommend, Chris, is: okay, you want to build a web app. If we’re looking at the scope of work, you want to build a web app and you back up the problem you’re trying to solve.

Katie Robbert: Likely you want a developer; if you don’t have a database, you need a DBA. You probably want a QA tester.

Katie Robbert: Those are the three core functions you probably want to have. What are you going to do with it?

Katie Robbert: Is it going to live internally or externally? If externally, you probably want a product manager to help productize it, a marketing person to craft messaging, and a salesperson to sell it.

Katie Robbert: So that’s six roles—not a hundred. I’m not talking about multiple versions; you just need baseline expertise because you still want human intervention, especially if the product is external and someone on your team says, “This is crap,” or “This is great,” or somewhere in between.

Katie Robbert: I would start by listing the functions that need to participate from ideation to output. Then you can say, “Okay, I need a UX designer.” Do I need a front‑end and a back‑end developer? Then you get into the nitty‑gritty.

Katie Robbert: But start with the baseline: what functions do I need? Do those come out of the box? Do I need to build them? Do I know someone who can gut‑check these things? Because then you’re talking about human pay scales and everything.

Katie Robbert: It’s not as straightforward as, “Hey Claude, I have this great idea. Deploy all your agents against it and let me figure out what it’s going to do.”

Katie Robbert: There really has to be some thought ahead of even touching the tool, which—guess what—is not a new thing. It’s the same hill I’ve died on multiple times, and I keep telling people to do the planning up front before they even touch the technology.

Christopher S. Penn: Yep.

Christopher S. Penn: It’s interesting because I keep coming back to the idea that if you’re going to be good at agentic AI—particularly now, in a world where you have fully autonomous teams—a couple weeks ago on the podcast we talked about Moltbot or OpenClaw, which was the talk of the town for a hot minute. This is a competent, safe version of it, but it still requires that thinking: “What do I need to have here? What kind of expertise?”

Christopher S. Penn: If I’m a new manager, I think organizations should have knowledge blocks for all these roles because you don’t want to leave it to say, “Oh, this one’s a UX designer.” What does that mean?

Christopher S. Penn: You should probably have a knowledge box. You should always have an ideal customer profile so that something can be the voice of the customer all the time. Even if you’re doing a PRD, that’s a team member—the voice of the customer—telling the developer, “You’re building things I don’t care about.”

Christopher S. Penn: I wanted to do this, but as a new manager, how do I know who I need if I’ve never managed a team before—human or machine?

Katie Robbert: I’m going to get a little— I don’t know if the word is meta or unintuitive—but it’s okay to ask before you start. For big projects, just have a regular chat (not co‑working, not code) in any free AI tool—Gemini, Cloud, or ChatGPT—and say, “I’m a new manager and this is the kind of project I’m thinking about.”

Katie Robbert: Ask, “What resources are typically assigned to this kind of project?” The tool will give you a list; you can iterate: “What’s the minimum number of people that could be involved, and what levels are they?”

Katie Robbert: Or, the world is your oyster—you could have up to 100 people. Who are they? Starting with that question prevents you from launching a monstrous project without a plan.

Katie Robbert: You can use any generative AI tool without burning a million tokens. Just say, “I want to build an app and I have agents who can help me.”

Katie Robbert: Who are the typical resources assigned to this project? What do they do? Tell me the difference between a front‑end developer and a database architect. Why do I need both?

Christopher S. Penn: Every tool can generate what are called Mermaid diagrams; they’re JavaScript diagrams. So you could ask, “Who’s involved?” “What does the org chart look like, and in what order do people act?”

Christopher S. Penn: Right, because you might not need the UX person right away. Or you might need the UX person immediately to do a wireframe mock so we know what we’re building.

Christopher S. Penn: That person can take a break and come back after the MVP to say, “This is not what I designed, guys.” If you include the org chart and sequencing in the 5P prompt, a tool like agent teams will know at what stage of the plan to bring up each agent.

Christopher S. Penn: So you don’t run all 50 agents at once. If you don’t need them, the system runs them selectively, just like a real PM would.

Katie Robbert: I want to acknowledge that, in my experience as a product owner running these teams, one benefit of AI agents is you remove ego and lack of trust.

Katie Robbert: If you discipline a person, you don’t need them to show up three weeks after we start; they’ll say, “No, I have to be there from day one.” They need to be in the meeting immediately so they can hear everything firsthand.

Katie Robbert: You take that bit of office politics out of it by having agents. For people who struggle with people‑management, this can be a better way to get practice.

Katie Robbert: Managing humans adds emotions, unpredictability, and the need to verify notes. Agents don’t have those issues.

Christopher S. Penn: Right.

Katie Robbert: The agent’s like, “Okay, great, here’s your thing.”

Christopher S. Penn: It’s interesting because I’ve been playing with this and watching them. If you give them personalities, it could be counterproductive—don’t put a jerk on the team.

Christopher S. Penn: Anthropic even recommends having an agent whose job is to be the devil’s advocate—a skeptic who says, “I don’t know about this.” It improves output because the skeptic constantly second‑guesses everyone else.

Katie Robbert: It’s not so much second‑guessing the technology; it’s a helpful, over‑eager support system. Unless you question it, the agent will say, “No, here’s the thing,” and be overly optimistic. That’s why you need a skeptic saying, “Are you sure that’s the best way?” That’s usually my role.

Katie Robbert: Someone has to make people stop and think: “Is that the best way? Am I over‑developing this? Am I overthinking the output? Have I considered security risks or copyright infringement? Whatever it is, you need that gut check.”

Christopher S. Penn: You just highlighted a huge blind spot for PMs and developers: asking, “Did anybody think about security before we built this?” Being aware of that question is essential for a manager.

Christopher S. Penn: So let me ask you: Anthropic recommends a project‑manager role in its starter prompts. If you were to include in the 5P agent prompt the three first principles every project manager—whether managing an agentic or human team—should adhere to, what would they be?

Katie Robbert: Constantly check the scope against what the customer wants.

Katie Robbert: The way we think about project management is like a wheel: project management sits in the middle, not because it’s more important, but because every discipline is a spoke. Without the middle person, everything falls apart.

Katie Robbert: The project manager is the connection point. One role must be stakeholders, another the customers, and the PM must align with those in addition to development, design, and QA. It’s not just internal functions; it’s also who cares about the product.

Katie Robbert: The PM must be the hub that ensures roles don’t conflict. If development says three days and QA says five, the PM must know both.

Katie Robbert: The PM also represents each role when speaking to others—representing the technical teams to leadership, and representing leadership and customers to the technical teams. They must be a good representative of each discipline.

Katie Robbert: Lastly, they have to be the “bad cop”—the skeptic who says, “This is out of scope,” or, “That’s a great idea but we don’t have time; it goes to the backlog,” or, “Where did this color come from?” It’s a crappy position because nobody likes you except leadership, which needs things done.

Christopher S. Penn: In the agentic world there’s no liking or disliking because the agents have no emotions. It’s easier to tell the virtual PM, “Your job is to be Mr. No.”

Katie Robbert: Exactly.

Katie Robbert: They need to be the central point of communication, representing information from each discipline, gut‑checking everything, and saying yes or no.

Christopher S. Penn: It aligns because these agents can communicate with each other. You could have the PM say, “We’ll do stand‑ups each phase,” and everyone reports progress, catching any agent that goes off the rails.

Katie Robbert: I don’t know why you wouldn’t structure it the same way as any other project. Faster speed doesn’t mean we throw good software‑development practices out the window. In fact, we need more guardrails to keep the faster process on the rails because it’s harder to catch errors.

Christopher S. Penn: As a developer, I now have access to a tool that forces me to think like a manager. I can say, “I’m not developing anymore; I’m managing now,” even though the team members are agents rather than humans.

Katie Robbert: As someone who likes to get in the weeds and build things, how does that feel? Do you feel your capabilities are being taken away? I’m often asked that because I’m more of a people manager.

Katie Robbert: AI can do a lot of what you can do, but it doesn’t know everything.

Christopher S. Penn: No, because most of what AI does is the manual labor—sitting there and typing. I’m slow, sloppy, and make a lot of mistakes. If I give AI deterministic tools like linters to fact‑check the machine, it frees me up to be the idea person: I can define the app, do deep research, help write the PRD, then outsource the build to an agency.

Christopher S. Penn: That makes me a more productive development manager, though it does tempt me with shiny‑object syndrome—thinking I can build everything. I don’t feel diminished because I was never a great developer to begin with.

Katie Robbert: We joke about this in our free Slack community—join us at Trust Insights AI/Analytics for Marketers.

Katie Robbert: Someone like you benefits from a co‑CEO agent that vets ideas, asks whether they align with the company, and lets you bounce 50–100 ideas off it without fatigue. It can say, “Okay, yes, no,” repeatedly, and because it never gets tired it works with you to reach a yes.

Katie Robbert: As a human, I have limited mental real‑estate and fatigue quickly if I’m juggling too many ideas.

Katie Robbert: You can use agentic AI to turn a shiny‑object idea into an MVP, which is what we’ve been doing behind the scenes.

Christopher S. Penn: Exactly. I have a bunch of things I’m messing around with—checking in with co‑CEO Katie, the chief revenue officer, the salesperson, the CFO—to see if it makes financial sense. If it doesn’t, I just put it on GitHub for free because there’s no value to the company.

Christopher S. Penn: Co‑CEO reminds me not to do that during work hours.

Christopher S. Penn: Other things—maybe it’s time to think this through more carefully.

Christopher S. Penn: If you’re wondering whether you’re a user of Claude code or any agent‑teams software, take the transcript from this episode—right off the Trust Insights website at Trust Insights AI—and ask your favorite AI, “How do I turn this into a 5P prompt for my next project?”

Christopher S. Penn: You will get better results.

Christopher S. Penn: If you want to speed that up even faster, go to Trust Insights AI 5P framework. Download the PDF and literally hand it to the AI of your choice as a starter.

Christopher S. Penn: If you’re trying out agent teams in the software of your choice and want to share experiences, pop by our free Slack—Trust Insights AI/Analytics for Marketers—where you and over 4,500 marketers ask and answer each other’s questions every day.

Christopher S. Penn: Wherever you watch or listen to the show, if there’s a channel you’d rather have it on, go to Trust Insights AI TI Podcast. You can find us wherever podcasts are served.

Christopher S. Penn: Thanks for tuning in.

Christopher S. Penn: I’ll talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights?

Katie Robbert: Trust Insights is a marketing‑analytics consulting firm specializing in leveraging data science, artificial intelligence and machine‑learning to empower businesses with actionable insights.

Katie Robbert: Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach.

Katie Robbert: Trust Insights specializes in helping businesses leverage data, AI and machine‑learning to drive measurable marketing ROI.

Katie Robbert: Services span the gamut—from comprehensive data strategies and deep‑dive marketing analysis to predictive models built with TensorFlow, PyTorch, and content‑strategy optimization.

Katie Robbert: We also offer expert guidance on social‑media analytics, MarTech selection and implementation, and high‑level strategic consulting covering emerging generative‑AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL·E, Midjourney, Stable Diffusion and Metalama.

Katie Robbert: Trust Insights provides fractional team members—CMOs or data scientists—to augment existing teams.

Katie Robbert: Beyond client work, we actively contribute to the marketing community through the Trust Insights blog, the In‑Ear Insights Podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking.

Katie Robbert: What distinguishes us? Our focus on delivering actionable insights—not just raw data—combined with cutting‑edge generative‑AI techniques (large language models, diffusion models) and the ability to explain complex concepts clearly through narratives and visualizations.

Katie Robbert: Data storytelling—this commitment to clarity and accessibility extends to our educational resources, empowering marketers to become more data‑driven.

Katie Robbert: We champion ethical data practices and AI transparency.

Katie Robbert: Sharing knowledge widely—whether you’re a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results—Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever‑evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.