AI agents have taken center stage in tech conversations over the past year. Bold claims swirl about how they’ll reinvent workflows, slash costs, and even replace human teams. But with so much hype in the air, it’s worth stepping back to ask: what are AI agents really doing today? And where are they actually headed?

This post is our attempt at a clear-eyed check-in. It captures the current landscape of AI agents: what they are, how they’re being used, and when they’re genuinely valuable. We hope it serves as a benchmark you can revisit months down the line to see what’s changed, and what hasn’t.

At their core, AI agents are already solving meaningful business problems.

Anthropic puts it well: “Agents are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.”

That control and autonomy set them apart from simpler AI tools—and open the door to tackling tasks that once demanded human time and attention.

Industry leaders are investing accordingly. OpenAI has introduced Operator, and Google launched Agentspace to help teams build agents more easily. A new layer is also emerging: Foundation Agents—custom-built, always-on agents designed to serve as the connective tissue between your business logic and the foundation models powering your AI stack.

But before FOMO nudges you into building something you're not ready for, it’s worth grounding yourself. This guide breaks down what AI agents really are, where they fit best, and what common traps to avoid. Along the way, we’ll highlight the different “flavors” of agents and explore where simpler alternatives—like structured workflows—might be the smarter bet.

The short version? Just because you can build an agent doesn’t mean you should. Knowing when not to use agent architecture might just be your best strategic decision.

What makes an AI agent, well… an agent?

Let’s start with the obvious: there's no single, universally accepted definition of what an AI agent is. Each major player—OpenAI, Google, Microsoft, Anthropic—offers its own spin. But if you zoom out, some common threads emerge.

• OpenAI describes agents as "systems that independently accomplish tasks on behalf of users." Autonomy is front and center here, the agent doesn't just respond; it figures out what needs to be done and does it.
• Google DeepMind calls them "software systems that use AI to pursue goals and complete tasks on behalf of users." Their focus is on purposeful action: agents have a goal, and they figure out how to reach it.
• Microsoft distinguishes agents from assistants, calling them "the new apps" for an AI-powered world—tools that can take action, not just give suggestions. Agents are framed as autonomous applications, not just helpers.
• Anthropic highlights the diversity in agent designs, noting that "‘Agent’ can mean anything from a fully autonomous system to a more scripted helper bot." They emphasize the range, from freeform decision-makers to process-bound executors.
• Academia leans on classic AI theory: a system is considered agentic "if it can tackle complex tasks without explicit supervision or direction," as stated in a recent Princeton paper.

Despite the differences in phrasing, the shared DNA is clear:

An AI agent is a system that can make decisions, take action, and pursue goals—often using tools or APIs—without being explicitly told every step.

In other words, agents aren’t just reactive. They’re proactive.

The five traits most definitions agree on:

1. Autonomous execution – They complete tasks with minimal input.
2. Goal orientation – They work toward specific outcomes.
3. Decision-making ability – They figure out their own next step.
4. Environmental awareness – They assess context and respond accordingly.
5. Tool use – They select and apply tools, apps, or APIs as needed.

The spectrum: not all agents are built the same

Not every agent is a full-blown, independent worker that runs for hours. Think of AI agents as existing on a spectrum, from narrowly focused automators to fully autonomous digital employees.

Here’s a quick tour through the types:

Most real-world implementations today sit somewhere in the middle: semi-autonomous agents that combine predefined logic with LLM-powered decision-making.

Autonomous agents (continuous)

These are the “holy grail” of agent design: systems that can run independently over time, navigating software, making decisions, and adapting to changing inputs.

They’re given a goal, not a script. From there, they might: search the web, open tabs, fill out forms, make API calls, loop until the job’s done.

Tools like OpenAI’s Operator and Google DeepMind’s Mariner are pushing this frontier. They simulate a human working at a computer: clicking, reading, and reasoning their way through complex workflows.

Use cases:

• Web research and summarization.
• Competitive analysis.
• Enterprise task orchestration (e.g. audit preparation, onboarding processes).

Task-driven agents (single-task or goal-specific)

These agents are more focused, they don’t improvise, but they do take initiative in how they complete their assigned task.

Examples:

• An AI scheduler that checks calendars, books rooms, and sends invites.
• A financial agent that monitors stock prices and executes trades within set parameters.

Many “copilot” tools fall into this category: autonomous in execution, but bounded in scope.

Use cases:

• Appointment scheduling.
• Transaction monitoring.
• Meeting summarization.

Agentic workflows

Somewhere between automation and agency, we find “agentic workflows.” These are structured processes powered by LLMs—but with most of the logic predefined.

Here’s how to spot them:

• The steps are clear and mostly fixed.
• The AI makes decisions only within those boundaries.
• If the steps are too rigid, it’s just a workflow. If the AI decides the steps, it’s an agent.

For example:

• A customer service process that uses LLMs to answer queries, create tickets, and draft responses.
• A pipeline where an AI writes code, runs it, evaluates the output, and then deploys if successful.

Anthropic notes that many real-world systems live here—not fully autonomous, but more flexible than Robotic Process Automation (RPA).

Embodied agents

These are agents with a physical presence. They operate in the real world via sensors and actuators: robots, drones, self-driving cars. They perceive, decide, and act based on their environment, just like their software-only counterparts, but with added complexity.

Examples:

• A warehouse robot navigating shelves.
• A smart vacuum mapping a room.
• An autonomous delivery drone.

While enterprise use of embodied agents is less common, the underlying principles are the same: perceive → reason → act.

Simple reflex vs. cognitive agents

This isn’t about format, it’s about intelligence.

• Simple reflex agents: If X, then Y. Think of them like thermostats or basic automation bots.
• Cognitive agents: These hold memory, understand goals, plan steps, and adjust strategies. Most LLM-based agents fall here.

Not all agents rely on LLMs, some use reinforcement learning, symbolic logic, or hybrids. But for enterprise AI today, LLMs dominate the agentic space.

TL;DR: Not all agents are created equal.

The key question is: How much freedom do you want to give your AI?

The more autonomy, the more potential, but also the more complexity, variability, and risk. Most practical implementations aim for that sweet spot: just enough agency to be useful, not so much that things go off the rails.

Where AI agents are already making an impact

While a lot of the AI agent conversation still lives in research papers and X threads, many companies are already using them, quietly, effectively, and without fanfare. Below are a few domains where AI agents are delivering real-world value today.

Finance & banking

AI agents are proving especially valuable behind the scenes:

• Monitoring for fraud and suspicious activity.
• Handling compliance checks and reconciliation.
• Automating routine reports and regulatory workflows.

Some firms use agent-like systems to scan market data and execute trades. Others deploy them for back-office automation, data entry, invoice matching, policy reviews. According to Deloitte, these early wins are driving serious productivity gains, even in risk-averse environments.

But full autonomy? Still evolving, especially in tightly regulated contexts where explainability and control matter.

Healthcare

AI agents are helping clinicians reclaim valuable time:

• Drafting clinical notes from doctor-patient conversations.
• Managing appointments, reminders, and common inquiries.
• Triage agents pre-analyzing radiology scans and suggesting draft reports.

Hospitals like AtlantiCare are already using systems that generate clinical documentation automatically with tools like Oracle Cerner. In research, agents are being used to accelerate drug discovery and genomic analysis, compressing hours of work into seconds.

These agents aren’t replacing doctors. But they are shifting time back to where it matters most: patient care.

Software development & DevOps

Code generation is just the tip of the iceberg.

Next-gen agents are learning to:

• Debug issues across build pipelines.
• Execute and test code snippets in real time.
• Auto-scale infrastructure based on usage patterns.

Tools like GitHub Copilot kicked things off, but now we’re seeing agents that operate across the full software lifecycle, from writing the code to deploying it and monitoring performance.

Even DevOps copilots can now adjust cloud resources or restart services autonomously when something goes wrong.

Customer service & support

Customer support has quietly become one of the most agent-ready environments:

• Handling full ticket lifecycles.
• Responding to complex account-related queries.
• Taking actions like issuing refunds or updating records.

Think less “chatbot” and more digital caseworker. These agents don’t just respond, they pull in data, make decisions, and follow through. CRM systems like Salesforce already integrate these capabilities at scale, enabling 24/7 support with full system access.

Business process automation

Forget the rigid bots of traditional RPA. AI agents bring a new level of flexibility:

• Understanding unstructured inputs.
• Navigating across systems without brittle rules.
• Making small decisions along the way.

Microsoft’s Power Platform already offers agent functionality that connects to enterprise systems and handles workflows like onboarding, inventory management, or expense reconciliation.

When AI agents aren’t a good approach

AI agents can be powerful, but they’re not always the right tool. In many cases, simpler solutions will outperform them in speed, cost, and reliability.

So when should you not use an agent?

• When the workflow is predictable: If your task follows a clear, repeatable path, automation or a single LLM call is usually more efficient.
• When reliability is critical: Financial systems, safety-critical workflows, and medical tools require consistency. Agents introduce variability by design.
• When you need transparency: In regulated industries, every action must be traceable. Agents don’t always offer clean audit trails.
• When scale and cost matter: Agents often involve multiple LLM calls, which can get expensive fast—especially for high-volume, low-risk tasks.

What to consider before building your first AI agent

Let’s say you’ve done the math and yes, an agent does make sense for your use case. Before jumping in, here are five things to keep in mind to avoid building something impressive… but ultimately unusable.

1. Scope and business alignment

The biggest failure mode for agents? Misalignment. It’s easy to get caught up in demos and prototypes that look amazing, but solve nothing meaningful. Agents are especially vulnerable to this trap because they feel magical. But without clear purpose, they quickly become overengineered experiments.

Before building anything, define:

• A real problem worth solving.
• A clear success metric tied to business value.
• A narrow scope for your first iteration.

Start small. Solve one thing well. Then expand.

We explored this idea in more detail in our piece on “The silent threat to AI initiatives”, a cautionary look at how lack of alignment derails otherwise promising projects.

2. Reliability isn’t guaranteed

Unlike traditional software, agents are non-deterministic. That means:

• Same input, different outputs.
• Complex behavior chains that are hard to debug.
• Failures that only show up at scale.

You’ll need to:

• Build extensive test suites (yes, for your prompts too).
• Monitor behavior continuously.
• Plan for unexpected edge cases.

Expect variability. Even well-designed agents can behave differently given the same input, especially at scale.

3. Hallucinations still happen

Even with today’s most capable models, hallucinations remain a risk, especially when:

• The domain is niche or specialized.
• The agent needs to “fill in” gaps.
• You’re using smaller or less-aligned models.

Mitigation strategies:

• Use RAG to ground outputs in real sources.
• Add guardrails to validate or flag content.
• Let agents communicate uncertainty (“I’m not sure” is better than confidently wrong).
• Involve humans for review in high-stakes or ambiguous cases.

We break down hallucination control strategies here.

4. Observability is everything

If you can’t see what your agent is doing (or why), it’s almost impossible to improve it.

At minimum, you’ll need:

• Logs of all steps and reasoning traces.
• Metrics on success/failure rates.
• Alerts when behavior changes unexpectedly.

Observability tools like Arize and Langfuse can help here.

5. Expect trade-offs

Agent-based systems introduce:

• More control? No.
• More complexity? Definitely.
• Higher costs? Often.
• Longer latencies? Usually.

You’re trading simplicity for flexibility, and predictability for adaptability. That can be a smart trade, but only if you’re prepared for it.

Avoiding pitfalls: common myths & misconceptions

Let’s bust a few persistent myths before you get hands-on with AI agents. These misconceptions can lead to wasted time, broken expectations, or overhyped internal demos that go nowhere.

Myth 1

❌ Myth: “AI agents don’t need human input”

✔️ Reality: Even the most advanced agents need a human in the loop.

Autonomy doesn’t mean chaos, most successful systems use tight scopes, predefined tools, human-in-the-loop input, and human fallback. Autonomy without control = risk.

Myth 2

❌ Myth: "AI agents will replace entire teams"

✔️ Reality: AI agents are here to amplify, not replace.

They automate the repetitive so teams can focus on the strategic. Creativity, judgment, and decision-making remain deeply human—agents just help unlock more of it. Organizations that embrace AI as a teammate, not a threat, are the ones pulling ahead.

Myth 3

❌ Myth: “Any company can plug in an agent out-of-the-box”

✔️ Reality: Agents need data access, system integration, and domain context.

The “magic” of agents only works when they can interact with your business logic, processes, policies, and infrastructure. That takes real effort from almost all the departments in a company.

Final thought

As AI, agents aren’t silver bullets. They’re powerful tools that can unlock serious impact when scoped properly, tested rigorously, and aligned to real business needs.

The hard part isn’t building the agent. It’s building the right one, for the right problem, in the right way.

This is the first post in a series where we’ll be unpacking the evolving landscape of AI agents, what’s working, what’s not, and how to make real progress in production.

The field is moving fast, and we’d love to hear from you:

• What are you curious about when it comes to AI agents?

• What challenges are you facing in your own implementations?

• Drop us a line or connect on LinkedIn to share your perspective, we’re listening!