Human-Agent Collaboration: What Are AI Agents Really Good At Today?

AI Agents Are Already Reshaping Productivity.

At ClickUp, we’re seeing how AI agents are taking on repetitive work, drafting docs, summarizing long comment threads, suggesting tasks from meetings, and even generating reports from raw project data. This isn’t just time-saving, it’s mind-saving. Teams can focus on strategy and creativity, not wrangling routine admin.

The Future? Autonomous Workflows Across Your Entire Workspace

Imagine an AI agent that doesn’t just respond to prompts, but proactively keeps your project on track. Need to reschedule a delayed task, reassign team members based on bandwidth, and ping a client with an updated timeline?

That’s where we’re headed. At ClickUp, we’re building toward a future where agents understand your workflows holistically and act on your behalf securely, contextually, and in real-time.

AI agents today are good at one thing: getting simple, repeatable tasks done without messing up or slowing down. At Assista, we use them to handle things like sending emails, creating tasks after meetings, updating spreadsheets, checking calendars, and moving information between tools.

They’re not smart in a human way, they just follow instructions really well. If you know what needs to happen, they’ll do it quickly and without complaining. That’s what they’re actually good at: doing the stuff you don’t want to spend your day on.

They’re not here to replace people, and they’re nowhere near ready to think or make tough calls. But they are saving us hours every week. Instead of chasing reminders or copying data between apps, our team can focus on work that actually needs thought.

The real value isn’t in pretending these agents are some kind of genius, it’s using them as quiet helpers in the background. No fluff. Just useful tools doing useful things.

Are AI agents hyped — or actually useful?

AI agents aren’t just flashy demos—they deliver real impact. For example, Connecteam deployed 11x’s AI SDR “Julian” and booked 20 meetings weekly, saved $450K/year, cut no‑shows by 73%, and boosted revenue per SDR by ~$30K/month. So yes—they’re useful—but only when supported by real data, human oversight, and thoughtful orchestration.

How should humans and AI agents truly work together today?

At Warmly, we combine our real‑time intent signals with 11x’s autonomous AI SDRs (like Alice & Mike) to power high‑volume, hyper‑personalized outreach 24/7. AI handles the research, segmentation, and first-touch messaging. But humans always set strategy, refine tone, and step in for high-intent accounts. That hybrid model—AI scaling execution, humans steering creativity and trust—is how we operate daily.

AI agents are best at open-ended, exploratory tasks like testing ideas, adjusting strategy, and dealing with ambiguity. AI workflows, in contrast, excel at structured, repeatable processes with clear rules.

From my experience developing AI-driven content operations, workflows are great for drafting, formatting, and SEO optimization.

But for researching a topic, figuring out what’s missing, finding the right data, and refining based on what turns up, that’s where agents shine.

The biggest trap for AI in Go-to-Market is the long awaited discovery of the holy grail to our revenue challenges.

Infinite growth, executed by agents.

The reality is: higher quantity of outreach, commoditization of tools, lower visibility and a red sea of competition.

The highest lever AI and AI agents have today are two fold:

First, increase in quality. With a fraction of the effort, we can be better educated as sellers, more personalized & relevant as marketers and summarize an insane amount of impact conversations in Customer Success.

Second, increase in value delivery: 80% of tedious, manual and brain draining research is moving from individuals in commercial teams to the RevOps functions where all insights accumulate and create better data and insights back to the organization at scale.

Most “AI agents” you see online are just workflows with a GPT prompt inside. They can generate an email or summarize a message, but they don’t have decision-making powers. That’s not an agent. That’s an automated workflow. And in most cases, automation is perfectly sufficient.
Real AI agents go a step further. They understand context, remember past interactions, make decisions, and take action in your tools.
The three areas where AI agents truly shine at the moment:
Customer support: An AI agent can answer questions based on your knowledge base and trigger real actions, like updating an order, pausing a subscription, or escalating to a human.

Lead generation and appointment setting: I’ve built AI agents that proactively sell to leads in the Instagram DMs and you wouldn’t be able to distinguish them from humans. They can take actions such as notifying the human team, or visiting the web to find more information.

Research: Agents can search, compare, summarize, and iterate. For example for lead research.
In other fields like content creation and marketing we mostly still see AI workflows that are rule-based workflows without decision making power. But as the technology is quickly evolving, we will soon hopefully see some great real use cases in those areas as well!

I will tell you what AI Agents are really good at today. Let me be direct: they’re brilliant at turning chaos into clarity.

AI agents today excel at automating complex, repetitive tasks across business functions, quickly and effectively. They can summarise documents, analyse data, write reports, extract key insights, manage emails, and even take actions across tools like Google Workspace or Microsoft 365. They’re not just helpful—they’re becoming essential for anyone looking to boost productivity and scale operations intelligently.

So, what exactly is an AI agent?

Think of it as a smart digital co-worker. An AI agent is a goal-driven assistant that combines reasoning, memory, and task execution. It doesn’t just answer questions—it performs tasks on your behalf. Unlike simple bots, agents can take multi-step actions, adapt to feedback, and work across multiple applications to get results. You give it a clear objective, and it works out how to get there.

I train professionals across the EU to understand Generative AI—from prompt engineering and flow engineering to building AI agents. These are agents that don’t just automate, but collaborate. Whether you’re in compliance, HR, finance, or marketing, AI agents help teams work smarter, not harder.

But here’s the secret: they don’t replace smart people—they elevate them.

For instance, in our AI Academy, participants (from interns to young executives) learn how to design AI workflows and build agents that free them from repetitive admin work. The result? More strategic thinking, faster delivery, and better outcomes.
2025 isn’t about using AI—it’s about orchestrating it.

And when these young professionals leave our workshops with hands-on experience in building real, automated AI workflows, they’re not just prepared for the future—they’re building it.

One more thing… AI agents aren’t “arriving soon” anymore, they’re already here. The only question is: will your team know how to use them?

Welcome to the Agentic Era.

AI agents truly stand out when they enhance the products people use and love. Not when they’re added just because they’re trendy.

Great AI agents amplify what a product already does best. They excel at quickly delivering reliable drafts and smoothly handling tasks. Humans then step in to guide, refine, or adjust the output. It’s a seamless partnership, where AI brings efficiency and execution, while people add insight, context, and direction.

Keeping users in control of how independently an AI acts is crucial. People should always feel they’re in control. Plus, interacting with AI often uncovers user preferences and needs that traditional analytics miss.

When integrated thoughtfully, AI complements human strengths, making both better and the whole experience feel naturally improved.

From my experience building and collaborating with AI agents, I’ve seen over and over how far you can get with Human-in-the-loop collaboration with AI Agents. One example is in software development environments like startups and scale-ups where humans can collaborate with AI Agents to prototype, build, re-build, expand and maintain software products and solutions at an amazing speed compared. An example of how far we have come in human-agent collaboration is the move from Vibe Coding to why I and others have started calling Context Coding.

While Vibe Coding as a term embraced playful intuition and rapid experimentation, but fell short of being able to produce production-ready products and solutions, Context Coding is kind of the grown-up sibling of Vibe Coding and is all about pairing the agility of AI agents with disciplined system thinking. It’s about being intentional – leveraging AI in a structured way, respecting architecture, iterating purposefully, refactoring regularly, and continuously refining your product with clear context.

Another example is Work2gether AI, an always-on leadership coach – built to support middle managers with real-time guidance, team insights, and tools grounded in proven team science. Together with the specifically trained AI Agent, the middle manager gets a coach that they can spar with and collaborate with to improve team engagement, collaboration and performance. But again it is not about just delegating the leadership and team transformation to the AI Agent, but a collaboration between human and AI agent to achieve goals together.

Yet, successfully leveraging AI agents in Human-Agent collaboration takes skill. You cannot effectively outsource something that you don’t have a good understanding of. In good collaboration with humans with sufficient understanding of the domain, task, process, etc. Human-Agent Collaboration can provide 5x or 10x results, and even open up for things that were previously impossible – and the technology behind AI Agents are getting better and better on a weekly basis. Achieving such results however demands thoughtful integration, clear boundaries, and intentional system design. Done right, AI agents aren’t mere assistants – they become multipliers, supercharging human creativity and strategic execution to levels we couldn’t reach otherwise.

Bottom line. AI Agents are amazing today – they have the potential to becoming teammates in many different fields. But to unlock their full potential, we need to approach them not with hype, but with design, discipline, and a deep understanding of the systems we’re trying to change. Whether you’re building products or transforming teams, the real opportunity lies in how you collaborate with AI Agents – not just that you do.

There’s general agents (Manus,chatGPT) and specialised agents (clay, evergrowth). The former are good for an array of things as per list below. While specialised agents are verticalised for specific use cases such as account-based personalisation or cold email.

Basic Multi-step tasks: e.g. searching for info online or through docs, analyzing it, creating documents with human intervention only at the end for QA

Tool orchestration: Connecting to multiple APIs and running a sequence (e.g. pulling data from CRM, creating report, sending via email)

Process monitoring: Watching for specific triggers and taking action automatically

Competitive intelligence: Continuously monitoring competitors, compiling reports and alerts.

Market research: Gathering data from multiple sources, cross-referencing information, and producing reports

Agents are still in their infancy as a technology, so if it follows the same trajectory as image gen AI (midjourney, sora, veo3) then we’ll see some impressive improvements soon.

Today’s AI agents excel not just at automating tasks—but at elevating human thinking. At Seidor, we’ve learned that the greatest value emerges when we combine their processing power with human judgment. It’s no longer about “what can AI do instead of us,” but rather, “what can we do better with it?”

In the mining industry, for example, we developed an AI copilot that assists field supervisors by identifying deviations in the use of critical equipment—helping prevent operational downtime. The agent doesn’t just analyze past data; it proposes hypotheses about root causes and downstream impacts. It doesn’t just give answers—it sparks reflection and action.

In retail, we implemented intelligent agents to monitor thousands of competitor prices in real time and recommend automated adjustments to maintain our client’s brand promise of “lowest market price.” Here, the agent acts as a dynamic orchestrator, freeing up the team to focus on strategy and customer experience.

In education, we’re working with institutions in Education industry to build smart tutoring agents that don’t just deliver content—they teach students how to think. Using a Socratic approach, these agents can assess reasoning and prompt deeper inquiry through questions, simulating a thoughtful educator’s guidance at scale.

The future isn’t just automation—it’s human amplification. The best AI agents don’t replace people; they help us make faster, better, and more confident decisions—together

AI agents are software that has evolved beyond the “if-then” strict logic of traditional tools. These agents use various input types (text, images, databases, internet searches, API connections, user interactions, etc.), apply decision-making based on predetermined instructions (prompts), and produce specific outputs through actions such as:

AI agents excel in situations where a non-significant error rate is acceptable. Since AI agents are software (“machines”) that transform imperfect inputs into imperfect results, we cannot expect outputs to be qualitatively superior to the initial data provided.

From a business perspective, agents fill gaps in manpower, skills, and availability, leading to significant increases in productivity, employee engagement, customer satisfaction, and, most attractive to investors, profits.

The paradox? Despite their ease of adoption, effective human-agent collaboration doesn’t emerge naturally. Employees can independently leverage no-code tools to build agents that increase personal productivity and create bandwidth for more complex tasks. This phenomenon has linear impact on business growth, but companies seeking exponential benefits must develop a strategic plan.

Core principles for successful human-agent collaboration:

Honestly, my thoughts on where AI agents are today is that a lot of it is really grossly misunderstood, but also underutilized for both simple and difficult tasks.

This Accessibility Bridge, or how we as Humans and AI as tools interact, is in my opinion, thinning.

From my experience implementing AI workflows for $10M+ companies, the most successful human-agent collaborations happen when you stop thinking of AI as a magic bullet and start treating it like someone that never gets tired but needs constant guidance.

Here’s what I tell my clients: “AI is not going to replace you – AI is going to replace you if you fail to use it as a tool.”

From an evolutionary perspective, humans became the apex predator because we learned to use tools for both physical tasks and cognitive processing. AI is just the next evolution of that capability.

In a sentence: I use AI to reduce the mental load of any and all repeatable tasks, simple and difficult.

After implementing hundreds of AI workflows both for my personal life as well as throughout GTM, RevOps, and ABM initiatives, I’ve found agents excel in two specific areas + my niche area of work:

Processing massive datasets to identify trends humans would miss
Analyzing customer behavior patterns across multiple touchpoints
Detecting anomalies in performance metrics or lead quality

Breaking down complex problems into atomic components (what we call “atom of thought” processing)
Cross-referencing multiple data sources for comprehensive analysis
Maintaining consistent logic across large volumes of decisions

Creating personalized, relevant outreach at scale while maintaining authenticity
Generating variations to the n’th degree to prevent fingerprinting in campaigns
Adapting messaging frameworks to different personas, industries, marketing UVPs, and TAMS
The biggest mistake I see companies make is assuming that the general run-of-the-mill AI can operate independently with simple instructions.
In a system, we call this GIGO: Garbage In, Garbage out.

Most AI “hallucinations” aren’t technical failures – they’re design failures.
When I build AI systems, I spend 60% of my time defining what the AI should never do. You have to tell it exactly what not to assume, what sources to avoid, and when to ask for human verification.

It’s a whole process of teaching a child “do not shove the square through the circle hole”, and “the circle does not fit into the square hole”

Now multiply that logic and learning by 100mil+ training exercises, while also having it learn.
The Systems Thinking Approach to Implementation
What separates successful AI implementations from expensive experiments is systematic design. I use what I call a “modular input, modular output” approach:

Understanding Layer: Define exactly what context the AI needs
Reasoning Layer: Establish the logical frameworks it should follow
Integration Layer: Set rules for how it processes conflicting information
Output Layer: Specify exact formatting and delivery requirements
This isn’t just about writing better prompts – it’s about architecting AI systems that integrate seamlessly with your existing business processes.

The future of human-agent collaboration isn’t about AI doing more tasks independently – it’s about creating seamless partnerships where each handles what they do best.

I see AI becoming increasingly sophisticated at handling the systematic, repeatable aspects of knowledge work while humans focus on strategy, relationship building, and complex problem-solving that requires judgment > logic.

The companies that win will be those that design these collaborations thoughtfully, with clear boundaries, robust oversight, and a deep understanding of where human insight remains irreplaceable.

The bottom line: AI agents are incredibly powerful tools, but they’re still tools. The magic happens when you combine AI’s computational power with human strategic thinking, relationship skills, and business judgment. That’s not just human-agent collaboration – that’s human-agent amplification.

Today, AI agents are best at technical tasks. Especially those that used to require a developer.

In the past year, I’ve used dozens of new tools that let someone with zero coding experience take an idea and turn it into functional software or a live website.

These aren’t experiments. I’ve shipped multiple applications and automations that my team now uses daily to improve both speed and quality.

Two of the most useful:

Get Chunks – Input a URL and it returns content segmented into the kinds of chunks AI and search engines use to understand pages.
Screenshot

The Ontologizer – Input a URL or raw text and get back Schema markup, key entities, and a simulated Query Fan-Out.

Both tools are publicly available on GitHub.

Here’s what’s important. I didn’t write a single line of code. I used Cursor, a coding agent. I gave it a goal. It made a plan and wrote the code. I gave feedback. It revised. And I ended up publishing working software through prompts alone.

AI agents like this aren’t hypothetical. They’re real, and they’re already reshaping what non-developers can build.

Are AI agents hyped — or actually useful?

AI agents aren’t just flashy demos—they deliver real impact. For example, Connecteam deployed 11x’s AI SDR “Julian” and booked 20 meetings weekly, saved $450K/year, cut no‑shows by 73%, and boosted revenue per SDR by ~$30K/month. So yes—they’re useful—but only when supported by real data, human oversight, and thoughtful orchestration.

How should humans and AI agents truly work together today?

At Warmly, we combine our real‑time intent signals with 11x’s autonomous AI SDRs (like Alice & Mike) to power high‑volume, hyper‑personalized outreach 24/7. AI handles the research, segmentation, and first-touch messaging. But humans always set strategy, refine tone, and step in for high-intent accounts. That hybrid model—AI scaling execution, humans steering creativity and trust—is how we operate daily.