Onboarding AI: Why Your Agents Need the Same Treatment as New Starters

I spend a lot of my time working with membership associations, with our product ReadyIntelligence and we're increasingly being asked to help organisations take their AI implementations to the next level. The ones that struggle aren't the ones with poor technology. They're the ones that treat agents like purely software rather than something more like colleagues.

The parallels with people management aren't just useful metaphors. They're the key to getting real value from these tools.

We know that AI often feels like magic (apart from when it spurts rubbish) and modern platforms can make the initial setup straightforward. But moving beyond a shiny DIY demo to something that delivers consistent value in production takes experience. The difference between a proof of concept and a working system is the same as the difference between a sensible answer in a job interview and someone who's been in the role for six years.

Deploying your first AI agent doesn't need to be complicated. For most membership associations, the natural starting point is a knowledge assistant. Your organisation is sitting on masses of knowledge: member resources, policy documents, guidance notes, FAQs, event archives, years of accumulated expertise. A well-configured agent that can answer questions for members or staff delivers immediate value without requiring you to rethink your entire operation.

Choose your platform with production in mind, even if you're starting small. There are tools out there that get you to a proof of concept, but when you try to operationalise them, when you need reliability, security, cost control and governance, they fall apart. The sophistication should be in the platform's capability, not in the complexity of getting started.

When we talk about AI agents, it helps to think about where they actually sit in your day-to-day operations. Most organisations end up with agents working across three distinct areas.

First, there are agents that your members or customers interact with directly on your website or member portal. Then there are agents embedded in the tools your team already uses: your CRM, document management, internal knowledge base. Finally, there are agents working behind the scenes, connecting to email, ticketing systems, meeting software, doing work you might never see directly.

Understanding this landscape early helps you plan which agents to build first and how they might eventually work together.

Think about how high-performing businesses scale their teams. They don't just add headcount. They design roles with clear responsibilities, they hire specialists who can go deeper than generalists ever could, and they build systems that let the right person handle the right task. The receptionist knows who to route you to. The support team handles support. The finance team handles finance. Everyone knows their lane, and the handoffs are smooth.

The same pattern plays out with AI agents.

As you mature, you'll likely move towards specialist agents with clearly defined roles, orchestrated by what I think of as a receptionist. An agent that understands who you are, understands your question, and routes you to the right colleague. A specialist doing one thing well will always outperform a generalist trying to do everything. The handoffs need to be seamless, the routing intelligent. The user shouldn't notice the complexity underneath. They should just notice that things work better.

We see this regularly in the implementations we're asked to improve. An organisation's internal assistant started as a generalist, one bot that could handle Team’s questions, project queries, support tickets, member information, the lot. It was loaded with context about every part of the business, accessible to everyone. And it worked, for a while. But the organisations getting the best results have moved to a model that mirrors how effective teams actually operate: clear specialisms, smart routing, and everyone playing to their strengths.

Before you build an agent, define the job. What's the scope? What are the boundaries? What systems does it need access to, and which should it leave alone?

This sounds obvious, but it's remarkable how often it gets skipped. The temptation is to build something general and see what happens. You wouldn't hire a person that way, and you shouldn't build an agent that way either.

Just as employees have different levels of access depending on their role and seniority, agents need permissioning that reflects what they should and shouldn't be able to do. This goes beyond which systems they can access. It's about what actions they can take, what data they can surface, and to whom. An agent answering member queries shouldn't have the same access as one supporting your finance team. An agent available to all staff might need tighter guardrails than one used only by senior leadership.

Think of it like the way you'd manage a growing team. New joiners get closer supervision. Experienced staff get more autonomy. But everyone still has a line manager, and everyone's work is still subject to review if concerns arise.

This is where we see organisations get the biggest wins. When you give an agent clear instructions, it stops spending compute working out how to approach problems through trial and error. The same question asked twice gets approached the same way, searched the same way, answered the same way. That consistency is what builds user trust.

We encourage organisations to build what we call skills: wrappers around tools that explain how to use them. When you get a question about membership benefits, search the system this way. When you get product questions, look in these knowledge sources first. It's the equivalent of a procedures manual.

If you hired someone and didn't give them standard operating procedures, didn't explain how your systems work, didn't tell them where to find things, they'd waste weeks figuring it all out themselves. Agents do exactly the same thing, except they figure it out on every single conversation if you haven't set them up properly.

“Get the onboarding right and you see immediate improvements in quality, cost and consistency.”

A staged rollout works brilliantly for building confidence. First, the agent is only available to its creator, who tests and validates its capabilities. Then it opens to a small group of colleagues who can trial it and provide feedback. Only then does it go wider.

At each stage, someone is checking the work and identifying where it falls short. If you have a ticket or case management system, an excellent way of testing is feeding old questions in then using an agent to compare the agent's answers with the human ones. This builds trust quickly because you can see exactly how the agent performs against real scenarios.

Reporting and oversight should flex over time too. Early on, you might want detailed logs of every interaction. As trust builds, that oversight can loosen. But it should never disappear entirely.

Just as you'd expect a team member to be accountable for their budget and efficiency, agents need cost limits and usage monitoring. Warning thresholds and hard limits help keep things sensible. After a certain number of messages, either the agent isn't doing a good job and you should try a different approach, or the conversation has drifted off-topic and you should start fresh.

One of the things that surprises organisations early on is how much value they can get when they right-size the model to the task. Conversely we've seen individual conversations reaching double-digit costs because users were pasting in entire email threads and treating the agent like a personal coach. That might be appropriate for an agent serving 100 staff, but not 10,000 members.

You don't need the most powerful model for everything. Match the capability to the job and you get better economics without sacrificing quality. Efficiency has to become a governance concern, not just a technical one.

Once an agent is up to speed, the job isn't done. You need visibility into how it's performing. Not just whether it's working, but whether it's working well.
You should be able to see what questions are being asked, how the agent is approaching them, where it's succeeding and where it's falling short. Not micromanaging every task, but having enough oversight to catch problems early and spot opportunities for improvement.

Agents should own metrics, just like any staff member owns a KPI. What's the resolution rate? How often are users satisfied? Where are the gaps in knowledge that keep coming up? These aren't technical metrics for the IT team. They're performance indicators that tell you whether your agent is delivering value.

The organisations that treat agent performance as a leadership concern are the ones that get sustained value rather than a flashy demo that quietly stops being used.

Good organisations turn individual experience into collective wisdom. Your agents should do the same. Look at patterns across user interactions and feed the useful ones back in. There are privacy considerations to work through, but the payoff is substantial.

Here's something that separates good implementations from great ones. When an agent learns something useful in a conversation with one team member (how to query a particular system, what an acronym means, where to find a specific type of information) that learning can be fed back into shared knowledge.

Imagine every new hire in your business independently figuring out how your systems work. That's what happens when agent learnings aren't synthesised back into the core system.

Where the data lives.

When you use models from OpenAI or Anthropic, your data travels to their infrastructure. For many use cases that's fine. But membership organisations often hold sensitive information: member records, financial data, safeguarding concerns, legal correspondence. Self-hosted models are worth considering for specific tasks where you need complete control over data residency. Think of it in terms of the tasks you'd trust to different people. You might be happy for a temp to handle general enquiries, but you wouldn't give them access to HR files.

When the brain changes.

AI models don't last forever. Providers retire older versions and release new ones. When your agent gets a new model, increase your observation for a while. Check their work more carefully. I think of it like a return to work scenario after a period of absence. Once you're confident, you can return to normal oversight levels.

When it doesn't work out.

Sometimes, despite your best efforts, an agent isn't delivering. Much like with employees, there are times when you have to acknowledge that this particular hire isn't right for the role. The good news is the rebuild is usually faster than you think. The prompts you've refined, the edge cases you've discovered, the knowledge gaps you've identified: none of that is wasted. You're starting from experience, not from scratch.

The next phase of AI is about treating these tools with the same thoughtfulness we bring to building teams.

The organisations that do well won't be the ones that deploy the most agents or use the most advanced models. They'll be the ones that onboard their agents properly, give them clear roles and boundaries, check their work, manage their costs, and progressively expand their autonomy as trust is earned.

They'll treat agents like colleagues rather than magic boxes.

None of this is magic, and there are no shortcuts. The tools keep getting better at making setup easier, but consistency, reliability and real operational value still come from experience. From learning what works and what doesn't. From building the muscle memory that turns a promising demo into something your organisation actually depends on.

That's what I'm watching as we head further into 2026, and that's where I believe the real value will be created.