AI agents handle the details. Can you still see the big picture?

That's only half right.

Yes, agents are excellent at handling individual tasks, sometimes messy ones. The car-buying agent didn't need perfectly organised information to work with. It made sense of a cluttered inbox, navigated inconsistent car dealership websites, and synthesised information across formats. You don't need to tidy up everything before assigning a task to an agent. That's part of the magic.

But 'the agent handles the mess' doesn't mean you disengage. It means your role evolves, and your organisation needs to be set up to support this new way of working too. The person or team directing the agent still needs to see the road, be aware of upcoming roadblocks, and be ready to take a detour to avoid getting stuck in traffic.

Think about a project manager (PM) working with an agent on their Jira board. The agent doesn't need perfect formatting or flawless ticket descriptions. It'll figure that out. But the PM needs to understand how their work fits together: which tasks connect to which initiatives, what's at risk and needs to be prioritised, and where their agent should be working. Having this kind of visibility and understanding of what's ahead is what turns a potentially vague instruction to an agent into a useful one.

Without it, you get blind delegation: handing an agent the keys without knowing where you're going.

Here's what blind delegation looks like:'Help me with this sprint.'The agent will try. It'll scan the backlog, maybe prioritise by due date, maybe flag some blockers. But it's guessing at what actually matters because you haven't told it what matters, and you haven't told it what matters because you can't see the full picture yourself.

Now here's what happens when you can actually see the road ahead and steer your agent properly:

Same agent, but a completely different destination. You've done the steering, and now, the agent can put the pedal down.

The car-buying agent didn't just find a good deal. It produced a set of outputs along the way: it compared offers, decided it needed to negotiate, and recommended the best dealership to go with. One instruction went in, and several new to-dos came out.

If we extrapolate and assume we'll have some kind of AI agents working on our teams and in our everyday work platforms with us in the near future, this multiplies quickly.

An agent summarising a client call might create five new action items across three different Jira project boards. An agent reviewing a backlog might suggest splitting one vague task into a dozen smaller ones.

This kind of triage used to take hours of our time every week, so it has value. But it creates a new problem.

Imagine a PM arriving on Monday morning to find that an agent spent the weekend triaging the backlog. There are now 30 new tasks sitting in a flat list. There's no clear hierarchy of priorities, no links to parent initiatives, and no indication of which ones ladder up to your big Q3 launch. A few are marked urgent, but the PM can't tell if that's 'customer waiting' urgent or 'the agent defaulted to that label' because it thinks everything is urgent.

The PM now spends an hour sorting through the agent's output before they can act on any of it. The engine was running just fine, but nobody was steering, and now there's a mess to clean up.

This is the agent steering problem compounding. Your unsteered agent missed the mark, added more work to your team, and didn't add any value.

Your agent-generated work needs guardrails and somewhere meaningful to land. It needs to align with your existing priorities and provide context to understand where to place new tasks. Without your vital organisational and project context, there's just going to be more confusion, less clarity, and a natural undercurrent of scepticism around using AI in your organisation.

It means you need to give yourself and your team a clear view of the road ahead, seeing how work connects across teams, projects, and goals, so you can steer agents with real context and make sense of their output without losing the bigger picture.

For Jira teams, that's where apps like Hierarchy for Jira come in. It gives you cross-project visibility into how projects break down, from high-level initiatives right down to the smallest subtask. You see where dependencies sit on your roadmap, and which items are drifting. The agent can read your Jira data regardless, but if you don't know about these gaps, you can't tell the agent about them either. Hierarchy for Jira is how you read it. You can see which work ladders up to what, spot risks before they become fires, and give agents direction that reflects reality.

For teams on monday.com, the same principle applies. Using apps that provide more accurate work structures, like Unlimited Subitems, ensures agent-generated tasks have a meaningful place on your project boards and that every task is connected to the higher-level project rather than left floating in a flat list.

Recurring Tasks, another Upscale app, gives you built-in checkpoints: you can automate recurring tasks so only work that needs to be on your board is. It saves cluttering your board with repeating items, gives your agents the right context at the right time, and frankly, saves you a lot of time too.

Integrate Plus for monday.com and Slack syncs your team's updates across both platforms, meaning your team can easily add comments to monday items or pull context into Slack. Wherever your agents are looking, they'll have the context they need.

The goal isn't to constrain agents but to give them—and your team—a full context of how your work connects. You'll be able to point your agents at the right problems with specificity, and trust they'll stay on track, thanks to the rich level of project context you've given them, rather than having to stop and start constantly.