What are AI agents: Your guide to automating in 2026
AI agents are changing how work gets done. Not someday – it’s happening right now, across the same kinds of processes your team deals with every day: onboarding new clients, routing approvals, tracking compliance, and following up on tasks that somehow never move on their own.
According to Gartner, over 33% of enterprise software applications will include agentic AI by 2028. That’s not a prediction about the distant future. And the organizations getting ahead of it understand something important: agentic AI is really not about replacing decisions. It’s about removing the friction that prevents those decisions from being acted on.
So what are AI agents, how do they actually work, and what separates the ones that deliver real value from the ones that add noise? That’s what this guide covers.
Key takeaways
AI agents are active participants in your workflows, not passive tools. They perceive inputs, make decisions, and take action, handling the execution work so humans can focus on judgment.
They differ by how much they can adapt. From simple rule-based systems to learning models that improve over time, the type of agent you deploy determines how much autonomy and intelligence you’re working with.
AI agents operate in a perception → decision → action loop. Each stage depends on data quality, system access, and clear workflow structure to function reliably.
Coordination across teams, tools, and stakeholders is the real challenge. Agents only deliver value when they’re embedded in structured, orchestrated workflows — not bolted on top of them.
Adoption requires structured workflows, clean data, and human oversight. Agents amplify whatever is already in your process, good or bad.
What AI agents are
AI agents are software systems that can perceive information from their environment, decide what to do with it, and act to move a process forward. They’re not passive tools that wait for a command. They’re active participants in a workflow: reading inputs, evaluating options, and taking the next step.
The clearest way to see the difference: traditional automation follows a fixed script. AI agents make decisions within that script. When an exception appears, a rule-based system stops unless that exception was built into the system. An AI agent adapts.
In practice, this shows up in processes like:
Customer onboarding, where the agent validates submitted documents, flags missing items, and routes the case for approval without someone chasing every step.
Compliance workflows, where the agent monitors for exceptions, escalates when thresholds are crossed, and logs every action for audit.
Data processing across multiple source systems, where the agent reconciles inconsistencies and surfaces anomalies before they reach a human reviewer.
Service delivery coordination, where the agent tracks task status across internal teams and external clients, and follows up when timelines slip
AI agents don’t replace the people in these processes. They handle the repetitive execution around decisions — the follow-ups, the routing, the status tracking — so humans can focus on the moments that actually need judgment.
The different types of AI agents
The different types of AI agents vary in how much they can understand, adapt, and independently decide. Here’s what each category means in practice.
Simple reflex agents
These agents respond to predefined conditions: if X happens, do Y. They’re fast and consistent, but they can’t handle anything outside their programmed rules.
Examples: spam filters that quarantine emails based on keywords; rule-based chatbots that return scripted responses to common support queries; automated invoice routing that triggers payment approval when an amount is below a threshold.
Model-based agents
Model-based agents maintain an internal representation of their environment. They consider context, not just the immediate input, when deciding what to do next.
Examples: predictive monitoring systems that flag when equipment metrics deviate from historical baselines; analytics agents that track operational patterns across multiple data sources and surface shifts before they become problems.
Goal-based agents
Goal-based agents evaluate multiple possible actions against a defined objective. They go beyond just asking “what should I do?”. They’re asking “what’s the best path to the outcome?”
Examples: logistics optimization systems that balance delivery windows, driver availability, and route efficiency in real time; scheduling agents that coordinate resource allocation across a project without requiring manual input.
Learning agents
Learning agents improve over time. They analyze outcomes, incorporate feedback, and adapt their behavior based on what worked and what didn’t.
Examples: recommendation engines that refine suggestions based on user behavior; adaptive AI assistants in workflow tools that learn approval patterns and flag exceptions earlier over successive cycles.
Understanding the different types of AI agents tells you how much autonomy you’re actually deploying, and how much structure you need to put around them.
How AI agents work
AI agents operate in a continuous loop: perceive → decide → act. Each stage feeds into the next, and the quality of the output at each stage determines whether the agent actually moves the process forward or creates noise.
Perception: collecting and interpreting inputs
The agent starts by pulling data from its environment. This includes API calls to connected systems, records from databases, form submissions, document uploads, and event triggers from upstream tools. The agent then interprets this data: identifying what’s relevant, what’s missing, and what signals should influence the next step.
In a client onboarding workflow, for example, this might mean reading a document upload, checking it against a verification checklist, and flagging a missing field before it reaches a reviewer.
Decision-making: reasoning toward the next step
Once the agent has its inputs, it evaluates what to do. For simple agents, this means matching conditions to rules. For more sophisticated agents, it means weighing multiple options against a goal, while considering timeline, stakeholder availability, dependencies, and risk.
A compliance workflow agent, for instance, doesn’t just check whether a form was submitted. It evaluates whether the submission is complete, whether it falls within the required window, and whether it meets the criteria that would trigger escalation to a senior reviewer. The agent prepares that context so the human who receives it can decide immediately, without needing to reconstruct the situation themselves.
Execution: acting across systems and stakeholders
The agent then takes action: triggering the next workflow step, sending a task to the appropriate person, updating a record, or escalating an exception. This is where AI agents, human stakeholders, and systems have to work together. The agent handles the mechanical execution. Humans handle the decisions that require context, authority, or accountability.
That coordination layer is where most real-world agentic implementations run into friction. Execution doesn’t happen in one system; it happens across teams, tools, and external parties, often in parallel. Platforms like Moxo are designed specifically for this: embedding AI agents inside multi-party workflows so that every handoff, approval, and follow-up is structured rather than improvised.
What AI agents can do, cannot do, and should not do
AI agents are precise tools. Understanding where they add value, and where they create risk if left unsupervised, is the difference between a deployment that works and one that quietly breaks things.
What they can do well
Automate multi-step workflows across systems.
They move work between tools, validate inputs at each stage, and ensure steps happen in sequence. An accounts payable agent, for example, can pull invoice data, match it against a PO, route it for approval if it’s within tolerance, and escalate it if it’s not — without a human touching it at any intermediate step.
Process large volumes of operational data.
An agent monitoring a logistics network can evaluate thousands of shipment status updates per hour, identify which ones are off-track, and trigger corrective actions faster than any human team could.
Coordinate multi-stakeholder tasks and approvals.
In client onboarding, an agent can manage the sequencing of document collection, compliance checks, and internal reviews — nudging each party when their step is due and escalating when deadlines slip.
Surface decision-relevant information at the right moment.
Rather than asking a manager to review a full data set, an agent can present a pre-analyzed summary with the key exception flagged and the recommended next step ready to approve or reject.
What they cannot do
Exercise genuine judgment on ambiguous or high-stakes decisions.
An agent can route an exception to the right person, but it cannot decide whether a contract clause creates unacceptable legal risk.
Compensate for bad data.
If the inputs are incomplete, inconsistent, or out of date, the agent’s outputs will be too. AI agents amplify whatever is already in your process including its weaknesses.
Operate reliably without structured workflows.
An agent deployed on top of an informal, ad hoc process will inherit all of that process’s inconsistencies.
What they should not do
Make autonomous decisions.
Make final decisions on approvals, exceptions, or outcomes that carry legal, financial, or ethical accountability. These require a human in the loop.
Operate without audit trails.
Every action an agent takes in a regulated context needs to be logged, reviewable, and traceable to an authorized decision.
Replace the coordination layer.
Agents that run independently of your orchestration platform create shadow processes, i.e., work that happens but isn’t visible, trackable, or accountable.
According to McKinsey, up to 30% of current work hours can be automated globally by 2030. The agents that create lasting value are the ones deployed within clear boundaries: handling execution, not judgment.
Examples of AI agents in enterprise operations
AI agents are already embedded in the workflows of most enterprise organizations. Here are examples of AI agents grounded in specific processes and industries.
Customer service: tier-1 issue resolution
For financial services and insurance, AI agents handle the first layer of customer service inquiries: account status, claim updates, document requests. They access live account data, resolve the query if it’s within their scope, and escalate to a human agent with the full conversation context and a suggested resolution if it isn’t. Resolution time drops; escalation quality improves.
Financial operations: transaction monitoring and fraud detection
Banks and payment processors use AI agents to monitor transactions in real time, flagging anomalies against behavioral baselines. When a transaction falls outside normal patterns — unusual geography, atypical amount, suspicious timing — the agent flags it for review or triggers a hold automatically, before the transaction completes. The agent acts within seconds; a human analyst reviews the flagged cases.
Operations: onboarding and compliance workflow management
This is where orchestrated AI agents deliver the most visible impact. In business process automation, agents coordinate the sequencing of tasks that span internal teams and external parties.
A professional services firm onboarding a new client, for example, uses agents to manage document collection from the client, route submissions to the compliance team, track review status, and trigger the next onboarding step once each milestone is cleared. No manual follow-up. No dropped handoffs.
Moxo’s AI agents are built specifically for this kind of multi-party coordination. They operate inside structured workflows, not outside them, so every agent action is tied to a specific step, a specific stakeholder, and a specific accountability. See how it works in practice.
HR and workforce operations: employee onboarding
HR teams use AI agents to orchestrate the steps that turn a signed offer letter into a fully onboarded employee: IT provisioning, policy acknowledgment, benefits enrollment, manager introductions, and compliance training. Each step is tracked, each deadline is enforced, and each delay is flagged automatically.
Personal productivity: individual task management
At the individual level, AI assistants handle scheduling, information retrieval, and task routing. These are smaller-scale examples of the same underlying capability: a system that acts on your behalf, within defined parameters, without requiring you to manage every step.
The best AI tools for enterprise agents
Running enterprise-grade AI agents effectively requires more than the agents themselves. You need the infrastructure that lets them operate reliably across people, systems, and processes.
Workflow orchestration platforms
Orchestration platforms coordinate actions across agents, humans, and systems, ensuring that each step in a process happens in the right sequence, with the right context, and with clear accountability. For example, Moxo is purpose-built for workflows that span internal teams and external clients, where coordination breakdowns are most costly.
The distinction that matters: an orchestration platform embeds AI agents inside your workflows. Everything else runs them alongside your workflows. The first approach keeps humans, agents, and systems in sync. The second creates coordination gaps.
AI development frameworks
Development frameworks like LangChain, AutoGen, and CrewAI allow engineering teams to build and deploy custom agents, defining goals, tools, and decision logic from scratch.
These are powerful for organizations with specific, non-standard use cases that off-the-shelf platforms don’t cover. They require engineering investment to build and maintain, and they don’t include the coordination layer that orchestration platforms provide. The two are complementary: frameworks for building agents, orchestration platforms for running them inside structured processes.
Data and decision intelligence platforms
Data platforms, such as Snowflake, Databricks, or enterprise BI tools, provide the layer that agents query to make decisions. They give agents access to historical records, real-time operational data, and predictive models. Without reliable data infrastructure, agents make decisions on incomplete information. The quality of your data layer directly determines the quality of your agents’ outputs.
If you’re looking to create enterprise-grade AI agents, then the answer isn’t a single product. It’s a stack: development frameworks for building agents, data platforms for grounding them, and orchestration platforms for running them inside the workflows where they actually create value.
Risks and challenges to watch out for with agentic AI
Data reliability and model accuracy
AI agents are only as good as the data they operate on. Incomplete records, inconsistent formats, and outdated information produce unreliable outputs. Before deploying agents on a process, organizations need to assess the quality of the data those agents will consume, especially if that data crosses system boundaries.
Security and compliance risks
Agents interact with sensitive systems, execute actions on behalf of users, and make decisions that affect external parties.
This creates real exposure around:
Access control - Which systems can the agent reach, and under what conditions?
Data privacy - What information does the agent handle, and how is it stored or transmitted?
Regulatory compliance - Are agent-driven actions logged in a way that satisfies audit requirements?.
These aren’t hypothetical concerns. They’re the first questions your compliance and security teams will ask.
Coordination across systems and stakeholders
This is the core operational challenge. AI agents don’t fail because their underlying models are wrong. They fail because the processes they’re embedded in lack the structure to support them.
Tasks get lost when there’s no clear ownership. Execution breaks down when agents operate across systems that don’t share state. Accountability disappears when there’s no audit trail.
These problems don’t go away by adding more capable agents. They require structured orchestration that ensures every agent action is tied to a defined step, a defined owner, and a visible record.
How Moxo integrates AI agents into business operations
The challenge isn’t building AI agents. It’s building AI agents that work inside real business processes: ones that involve multiple teams, external clients, shifting timelines, and accountability at every step.
Moxo is a process orchestration platform built for exactly this. It brings together AI agents, human stakeholders, and external parties inside a single, structured workflow environment. Agents handle the execution; things like document collection, status tracking, escalation routing, follow-up sequencing, etc. Humans handle the decisions surrounding them. And every action, by every party, is visible, logged, and tied to a specific process step.
In practice, this means:
Client onboarding workflows that move from signed agreement to fully onboarded client without manual coordination at every step.
Compliance processes where exceptions are automatically routed, escalated, and resolved, with full audit trails.
Integrated workflows that connect agents to the systems your team already uses, so nothing falls through the gaps between platforms.
AI agents that operate outside a structured orchestration layer create fragmented execution. Moxo ensures they operate inside it. Get started with Moxo to see what orchestrated AI execution looks like for your operations.
FAQs
What are AI agents used for in businesses?
AI agents automate the execution work around business processes: document validation, task routing, approval coordination, follow-up sequencing, and exception handling. They handle repetitive steps so human teams can focus on decisions that require judgment and accountability.
What are autonomous AI agents?
Autonomous AI agents operate with minimal human direction, perceiving inputs, deciding on the next action, and executing it without step-by-step instruction. In enterprise contexts, they typically operate within defined boundaries and with human oversight for approvals, exceptions, and high-stakes decisions.
What are the best AI agents for enterprise use?
The best enterprise AI agents integrate tightly with your existing systems, operate inside structured workflows, and maintain clear accountability for every action they take. Effectiveness depends as much on orchestration infrastructure as on the agents themselves.
What are the best AI tools for enterprise agents?
A practical enterprise AI stack combines workflow orchestration platforms (like Moxo) for coordination, AI development frameworks (like LangChain or AutoGen) for custom agent builds, and data intelligence platforms (like Snowflake or Databricks) for grounding agent decisions in reliable data.
What are the challenges with AI agents?
The primary challenges are data quality, integration complexity, and coordination structure. Agents deployed on unreliable data produce unreliable outcomes. Agents deployed without orchestration create fragmented execution. The organizations that get the most from agentic AI are the ones that treat it as a process design challenge, not just a technology decision.
