Advanced Automation: Moving to AI Agents

AI agents: definition, how they work, and the shift to autonomy

AI agents mark a clear step-change in digital automation: you no longer simply ask them to respond, but to achieve a goal. According to IBM, an artificial intelligence agent is "a system or programme capable of autonomously performing tasks on behalf of a user or another system" (source). In practical terms, that means: research, analyse, decide and act — without human intervention at every micro-step. You move from a "conversation" mindset to an "executed workflow" mindset.

What really changes: from conversational support to action

A traditional conversational chatbot mainly handles short-term interactions: understand a question and produce an answer. By contrast, an agent targets an outcome and can plan sub-tasks, choose tools, gather missing information and self-correct as it executes (IBM). Google Cloud captures the idea well: an agent is goal-oriented and stands out through autonomy, planning and executing actions — not just generating text (source).

In business, the difference is operational: a chatbot "responds", an agent "does" (or triggers) within a controlled framework. This ability to chain steps together boosts productivity, but it also changes governance: permissions, approvals, audit trails. That is where many pilots succeed or fail: not on the demo, but on integration and control.

Artificial intelligence agent, intelligent agent, LLM agent, and agentic systems: clarifying the vocabulary

You will often see these terms blurred together, particularly in English-language literature. Here is a clear framing:

• Artificial intelligence agent: a generic term (IBM, Google Cloud) for goal-oriented autonomous software.
• Intelligent agent: a more "historical" phrasing (classic AI) emphasising perception, decision-making and action in an environment.
• LLM-based agent: an agent whose "brain" relies on a large language model, with tool calls running in the background (IBM).
• Agentic system: a broader setup (rules, memory, tools, supervision, sometimes multiple agents) designed to execute workflows.

IBM notes that LLMs sit at the centre of many modern agents — hence "LLM agents" — but the key difference comes from tool use and the ability to orchestrate sub-tasks autonomously (source). Google Cloud also outlines four recurring building blocks: persona, memory, tools and model (source).

Agentic commerce: when AI orchestrates an end-to-end buying journey

Agentic commerce describes a near-term future where agents become the "customers" (or their delegates). Practically, they discover, compare, negotiate constraints, build a basket and initiate the transaction. For brands, the challenge is no longer just to be "visible": you must be selectable and actionable by systems that synthesise and act.

This paradigm comes with heavy prerequisites: standardised exchanges, interoperability across providers and, above all, a trust layer. Who authenticates the agent? Who delegates spending authority? Who bears responsibility? In this context, trusted intermediaries (banks, identity providers, consent frameworks) become central.

Concrete example: travel booking and a chain of providers

Imagine a traveller saying simply: "I want to book four days in Rome." A chain of specialist agents spins up: search, comparison, budget and constraints optimisation, transport booking, hotel selection, activities, restaurants, then payment. Strong confirmation (for example Face ID) finalises the purchase in a conversation and a click.

We are not at mass scale yet: we need a standardised ecosystem between hoteliers, distributors and suppliers, plus robust authentication, authorisation and payment mechanisms. The example is valuable because it makes multi-step, multi-actor orchestration tangible. And it points to a world where having the "best page" is not enough if your offer is not usable by the agent that executes.

How an AI agent works in practice

There is nothing mystical about it: an agent is a system that loops between planning, execution and verification. IBM describes a process where the agent receives goals and rules, breaks the task down, uses tools (external data, APIs, web search, other agents), then learns via feedback and memory (source). Google Cloud adds key capabilities: reasoning, execution, observation, planning, collaboration and self-improvement (source).

The "observe → plan → act → verify" loop

A useful way to understand agents is to think in loops. A simple framing often used is: observe, decide, act, observe again, adapt. This cycle is what differentiates a "generative" system from an "agentic" one: it does not just produce an answer, it drives a trajectory.

Tools, APIs and environments: how an agent executes real actions

Modern agents rely on tool calling: instead of answering purely from training data, they query external sources and trigger actions (IBM). Typical tools include web search, databases, internal APIs, business systems — even other specialised agents. This bridge to the outside world is what turns an AI that "talks" into an AI that "operates".

This has a direct implication: every tool is a risk surface (security, permissions, cost, mistakes). An agent that can write into a system must be treated like a full-fledged software actor: unique identity, least-privilege permissions, logs, limits, and controlled stop rules (IBM).

Memory, context and retrieval-augmented generation (RAG)

Without memory, an agent is brittle: it repeats itself, forgets, and fails to learn. IBM highlights that memory and feedback mechanisms enable iterative refinement and help avoid repeating errors by storing learned information (source). Google Cloud describes different memory types (short-term, long-term, etc.) for maintaining context (source).

In enterprise setups, this often materialises through retrieval-augmented generation (RAG): the agent fetches up-to-date internal documents and uses them to reason and act. The critical point is not "having lots of documents"; it is ensuring access rights, freshness, traceability of sources and the quality of what is retrieved. The more you industrialise, the more this documentation layer becomes a strategic asset.

Control, guardrails and human oversight: where to put the "stop"

An agent can move quickly… and quickly in the wrong direction. IBM recommends concrete practices: accessible activity logs, controlled interruption, unique identities, and human approval for high-impact actions (source). Google Cloud also points to limitations in high-stakes ethical situations and unpredictable environments, as well as potentially high compute costs (source).

• Technical stop: timeouts, action quotas, iteration limits.
• Business stop: thresholds (amount, volume, risk), mandatory approvals.
• Compliance stop: masking sensitive data, access policies, auditability.

Types of AI agents: understanding architecture patterns

Talking about an "agent" in the singular hides a key reality: there are multiple architectures, from the simplest (reflex) to the most advanced (learning, multi-agent). IBM proposes five types, from simplest to most sophisticated: simple reflex, model-based reflex, goal-based, utility-based, learning (source). To reflect current enterprise usage, it is also helpful to add two common families: tool-using task agents and multi-agent systems.

The 7 most-used families of AI agents

Here is a pragmatic overview that links theory and modern implementations. Use this grid to choose the right architecture based on autonomy level, constraints and criticality.

Simple reflex agents

They apply "condition → action" rules with no memory. IBM describes them as effective in fully observable environments and uses a simple thermostat-style example (source). They are robust, but limited as soon as context becomes partial or dynamic.

Model-based reflex agents

They add memory and maintain an internal model of the world, updated through observations. IBM uses the example of a robot vacuum that remembers cleaned areas and adjusts its path around obstacles (source). You gain adaptability, but remain rule-constrained.

Goal-based agents

They plan a sequence of actions to achieve a goal. IBM gives the example of a navigation system that compares routes and changes its recommendation if a faster option appears (source). This maps well to workflows with a defined, measurable outcome.

Utility-based agents

They optimise trade-offs using a utility function (time, cost, complexity, risk, etc.). IBM illustrates this with navigation arbitrating between energy efficiency, traffic and tolls (source). In B2B, this becomes powerful as soon as you have multi-criteria decision-making.

Learning agents

They store experience and improve with feedback. IBM describes four elements (learning, critic, performance, problem generator) and cites e-commerce recommendations improving based on stored activity (source). Their value grows over time, but governance and evaluation become non-negotiable.

Tool-using autonomous task agents

This family matches modern agents that combine a model (often an LLM) with tools: search, APIs, internal databases and action execution. IBM stresses that background tool use enables up-to-date information, workflow optimisation and automatic sub-task creation without human intervention (source). It is often the most useful enterprise format because it connects the agent to reality.

Multi-agent systems

Instead of one generalist agent, you organise a team of specialist agents. IBM notes that multi-agent frameworks tend to outperform single agents because they collaborate and fill information gaps (source). Google Cloud similarly describes multi-agent systems as setups where multiple agents collaborate (or sometimes compete) and may use different models depending on the need (source).

Other practical lenses: "employee" agents, "customer" agents, "data" agents, "code" agents

Beyond architectural types, you can also classify agents by business role to help prioritise. Google Cloud notably outlines six enterprise use families: customer agents, employee agents, creative agents, data agents, code agents and security agents (source). This structure helps you organise a portfolio of use cases and define KPIs per family.

• Employee agents: internal support (HR, IT), repetitive task automation.
• Customer agents: support, self-service, guided journeys.
• Data agents: collection, cleaning, synthesis, alerting.
• Code agents: development support, testing, refactoring (with guardrails).

Multi-agent orchestration: making an AI team work without losing control

Multi-agent orchestration means distributing a global objective across several specialised agents, coordinated by a plan and rules. IBM cites reasoning and orchestration patterns such as ReAct (iterative reasoning + action) and ReWOO (advance planning to reduce complexity and consumption), among others (source). The goal is not to stack components: it is to make execution reliable, traceable and economically sustainable.

When to move from a single agent to multiple agents: decision signals and criteria

Multi-agent is relevant when the mission becomes too broad for one agent, or when you need cross-checks. IBM highlights that collaboration and information sharing make agents more versatile than traditional models (source). But there are also risks: shared dependencies and shared failure modes.

Roles, coordination and conflict resolution: planning and trade-offs

A multi-agent system works well when each agent has a clear role, explicitly listed tools, and defined handover rules. In a clean orchestration, a coordinator agent can: break down the plan, assign sub-tasks, aggregate results, and request approval before sensitive actions. IBM stresses continuous re-planning: the agent must be able to correct course if a tool fails or new information invalidates the plan (source).

1. Define roles (e.g. research, analysis, execution, review).
2. Set priority rules (cost, time, risk, business impact).
3. Plan an arbitration mechanism for disagreements (vote, "judge" agent, human).

Traceability and observability: logs, evaluation and decision replay

Without observability, you cannot steer anything. IBM recommends providing users with an action journal (tools called, agents invoked, etc.) to strengthen transparency and support error detection (source). In production, this becomes a requirement: audit, compliance, debugging and continuous improvement.

• Action logs: what happened, when, with which tool, and the result.
• Decision traces: why the agent chose a given option (criteria).
• Replay: the ability to rerun a scenario to reproduce and fix issues.

Scale, latency and cost: avoiding loops and hidden complexity

IBM warns about infinite feedback loops: an agent may reuse the same tools repeatedly if it cannot plan or interpret results, which can require real-time human supervision (source). IBM also notes that computational complexity can be high: building effective agents takes time and can be expensive in resources, and some tasks can take several days depending on complexity (source).

To keep things industrialisable, you must manage three constraints at once: latency (experience), cost (budget) and reliability (risk). Advance-planning patterns such as ReWOO aim precisely to reduce token consumption and complexity while limiting the impact of intermediate failures (source).

B2B use cases: where agentic automation creates the most value

Goal-oriented agents are particularly effective for repetitive, multi-step, tool-heavy tasks. Google Cloud cites productivity gains through specialisation and parallel execution, as well as automating repetitive tasks to free up human time (source). IBM also points to use cases in healthcare, finance, supply chain and emergency response, with an emphasis on large-scale automation (source).

Marketing and content: research, framing, production, quality control

In marketing, the value lies in turning long chains (monitoring → insights → framing → production → QA) into measurable workflows. An agent can research, synthesise, suggest an angle, prepare an outline, then check consistency and compliance before publishing. The success condition remains input quality: brand data, editorial rules, legal constraints and reliable sources.

• Research and synthesis on a topic (with sources explicitly cited).
• Brief generation for multiple audiences.
• Quality control: duplication, structure, missing elements, tone.

SEO and GEO: structure, measurement, and reusability in generative AI engines

In a world where generative engines summarise and recommend, the challenge goes beyond ranking: you also need to be quotable and reusable. That requires rich content, evidence, clear structure and data that automated systems can exploit. Google Search Console and Google Analytics remain essential measurement layers to connect visibility, behaviour and business impact.

Practically, agents applied to SEO/GEO help industrialise loops: analyse → prioritise → produce → verify → measure. If you are preparing for agentic commerce, this becomes foundational: the agent (as buyer) must be able to understand your offer, compare it, and activate it with coherent, up-to-date information.

Sales and ops: qualification, enrichment, summaries, workflows

Sales and operations workflows are well suited to automation: lead qualification, account enrichment, meeting preparation, call notes, follow-ups and task creation. IBM mentions real-time data analysis and process optimisation in finance and supply chain, provided stronger security measures are in place for confidentiality (source).

The key is aligning autonomy with governance: you do not grant the same permissions to a preparation agent (read) as you do to an execution agent (write). In enterprise B2B, granular permission management is what makes scaling possible.

Support and back office: ticket handling, triage, routing and compliance

Support is a natural fit: understand a request, retrieve context, apply policy, execute, then verify. IBM cites integrating agents into websites and applications to improve customer experience, as well as healthcare use cases to free up time for urgent tasks (source). Back-office work (document triage, compliance, routing) also benefits from repeatable action chains.

Be careful: support quickly involves sensitive data. Confidentiality, logging and access controls are not optional — they are operational prerequisites.

How to create an AI agent: a step-by-step method (without unrealistic promises)

Creating an agent is not about "plugging in a model". You must define a goal, authorised actions, data context, an evaluation strategy and governance. IBM notes that an autonomous agent needs objectives and rules predefined by humans, even if it makes decisions independently during execution (source). For a deeper implementation view, see our guide how to create an AI agent and our resource AI agent training.

Step 1: define a measurable goal and acceptance criteria

An agent is goal-oriented: the goal must be explicit (Google Cloud). Avoid vague aims ("improve support") and prefer testable criteria. Example: "resolve an order-tracking request without escalation, using up-to-date internal sources, with a complete action log".

• Main objective (expected outcome).
• Constraints (time, compliance, tone, scope).
• Acceptance criteria (pass/fail tests).

Step 2: choose authorised actions (read, write, pay, approve)

Autonomy is not binary: you tune it. Define precisely what the agent can do, and when you require human approval. IBM recommends human approval for high-impact actions (mass sends, finance, etc.) (source).

Step 3: design data and context (sources, permissions, freshness)

Performance depends directly on the available data, its freshness and structure. IBM emphasises using tools and external resources to get up-to-date information, as well as keeping the knowledge base current (source). On the enterprise side, you must also model who can see what — and trace every access.

• Inventory sources (docs, databases, APIs) and define the source of truth.
• Define access policies (RBAC, least privilege).
• Manage freshness: what must be real-time vs periodic.

Step 4: test, evaluate, iterate (scenarios, failure modes, security)

A reliable agent is a tested agent. IBM notes the importance of data governance and rigorous training/testing processes, especially in multi-agent setups where weaknesses may be shared (source). Test normal cases, but above all failure cases: API downtime, conflicting data, ambiguous requests, abuse attempts.

1. Build a scenario set (success, failure, edge cases).
2. Measure accuracy, time, cost, escalation rate and error rate.
3. Strengthen guardrails and retest.

Step 5: deploy and govern (monitoring, updates, continuous improvement)

Once in production, the agent becomes a living system. IBM recommends activity logs, unique identities and the ability to interrupt — which implies monitoring and procedures (source). You also need continuous improvement: user feedback, error analysis and updates to knowledge bases.

For a sustainable deployment, formalise governance: a business owner, a security owner, an evolution process and exception rules. This framework is often what separates a "spectacular" pilot from a system that is genuinely used.

Risk, security and trust: what to lock down before deploying AI agents

The more an agent can act, the more you must secure it. IBM lists risks including multi-agent dependencies, infinite loops, computational complexity, and — above all — data privacy/security if integrations are poorly managed (source). Google Cloud also notes limitations in tasks requiring deep empathy and in high-stakes ethical situations, as well as potentially high costs for sophisticated agents (source).

Privacy, compliance and access management

Treat the agent like an application user with tightly controlled privileges. IBM recommends unique identities to improve traceability (origin: developers/deployers/users) (source). Put in place clear access segmentation, secret management and logging policies that stand up to audit.

• Dedicated identities and service accounts (no shared accounts).
• Least-privilege permissions with periodic reviews.
• Logging and retention aligned to your audit requirements.

Bias, hallucinations and execution errors: prevent rather than fix

The risk is not only "a wrong answer" — it is a wrong action. An agent can fill information gaps via resources, but it can also choose the wrong source, misinterpret a rule, or execute in the wrong place. IBM stresses the value of feedback (other agents or human oversight) and memorising solutions to avoid repeating errors (source).

Prevention means controls "before action" (approval, simulation) and "after action" controls (verification, rollback where possible). And on sensitive workflows, you should enforce human approval, especially early on.

Authentication, authorisation and payment: the role of trusted third parties

Agentic commerce makes these issues unavoidable. For an agent to pay or commit to a transaction, you need: a verifiable identity, explicit consent, delegated permissions and traceability. IBM underlines the importance of unique identities and human oversight for high-impact actions (source).

In payment journeys, trusted third parties (often banks) can play a critical role: authenticating, securing delegation, governing authorisations and providing strong validation mechanisms. Without this layer, full autonomy will remain limited to low-risk actions.

Preparing your business for the agentic era: content, structured data and visibility

If agents become buyers, comparators or recommenders, your digital presence must be interpretable and actionable. You are only one link in an orchestrated decision chain. Today's priority is to lay AI-compatible foundations — rich content, structured data, consistent information and trust signals.

To go further, read our resources on agentic AI, on autonomous AI agents and on AI agents for business.

Becoming "readable" by AI: entities, evidence, structure and consistency

"Readable" means: understandable, verifiable and reusable. In practice, the agent must be able to identify your entities (products, services, locations, offers), understand your terms, and find evidence (references, specifications, prices, lead times, guarantees) without ambiguity. The more structured your pages are, the more usable they become for systems that synthesise.

• Content built around real questions (use, limits, comparisons, decision criteria).
• Relevant structured data and consistent information across all touchpoints.
• Evidence: sources, figures, policies, terms, visible update dates.

Measurement: connecting visibility, acquisition and business impact

In an agentic environment, you must connect visibility to business performance — otherwise you are flying blind. Measure what you control: SEO performance, conversions, per-page contribution and engagement signals, using Google Search Console and Google Analytics. Then industrialise improvement loops: what you publish, what you update, what you retire.

The key is not having "more data": it is having actionable indicators tied to clear decisions (prioritisation, trade-offs, investment). That is also how you prepare your content to be cited, summarised and recommended in generative answers.

A quick word on Incremys: structuring SEO + GEO and industrialising execution with built-in agents

Incremys (B2B MarTech) states that it has integrated AI agents at the core of its platform since version 3.0, with an execution- and governance-first approach. The goal is not to "replace" your teams, but to structure repeatable, measurable and governable workflows for next-generation SEO that includes GEO (visibility in generative AI engines). For the marketing angle, you can also read AI agents for marketing.

Conversational research, multi-persona briefs, a dual SEO + GEO quality score, and contextual GEO recommendations

The use cases mentioned by Incremys address very practical needs: conversational keyword research with a personalised AI, multi-persona brief generation, a dual SEO + GEO quality score, and contextual GEO recommendations. This kind of integration reflects reality: the hard part is not just finding ideas — it is prioritising, producing at scale, and proving impact.

In an agentic trajectory, these building blocks (data, structure, method, measurement) become foundations. Those who lay them early become inherently more "selectable" as AI-orchestrated journeys become mainstream.

FAQ about AI agents

What are AI agents?

They are goal-oriented software systems that can autonomously perform tasks on behalf of a user or another system. IBM defines them as programmes capable of executing tasks autonomously by designing workflows via available tools (source). They can plan, act through tools, and adapt using memory and feedback.

What exactly is an AI agent?

It is an AI system that does not stop at generating text: you give it a goal, and it carries out a sequence of actions (research, API calls, decisions, execution) to reach that outcome, with rules and guardrails. Google Cloud stresses this goal orientation and the ability to execute actions — not just respond (source).

How does an AI agent work?

An agent works as a loop: it receives a goal and rules, plans, uses tools (external data, APIs, search), executes, then verifies and adjusts. IBM describes planning, tool-assisted reasoning, then learning and reflection through feedback and memory (source). LLM-based agents often rely on tool calling to access up-to-date information and complete complex tasks.

How is an AI agent different from a chatbot?

A non-agentic chatbot mainly handles conversational exchanges and targets short-term goals. IBM notes that these chatbots typically have neither tools, nor memory, nor advanced reasoning: they do not plan and require continuous intervention (source). An agent can plan sub-tasks, choose tools, self-correct and execute actions in external systems.

What are the main types of AI agents?

Typologies vary, but a strong baseline comes from IBM: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents and learning agents (source). In practice, many teams also add tool-using task agents and multi-agent systems, which are very common in enterprise contexts.

What are the 7 types of AI agents?

A useful theory-plus-practice grid groups: (1) simple reflex, (2) model-based reflex, (3) goal-based, (4) utility-based, (5) learning agents (IBM typology), plus (6) tool-using autonomous task agents, and (7) multi-agent systems (collaboration between specialised agents). This classification helps you choose the right level of autonomy and complexity for your context.

How do you create an AI agent?

Start with a use case that has clear value and controlled risk, then follow a method: measurable goal, authorised actions, data and context, testing, deployment and governance. IBM reminds us that an agent needs objectives and rules predefined by humans, even if it makes decisions autonomously during execution (source). For a detailed approach, see how to create an AI agent.

What is multi-agent orchestration and when should you use it?

Multi-agent orchestration is about getting several specialised agents to work together towards a global objective, with coordination, handovers and control. IBM notes that multi-agent frameworks tend to outperform single agents because they collaborate and fill information gaps (source). Use it when a mission requires multiple skills, cross-checks, or parallelisable execution.

What is an LLM-based agent and what is it used for?

It is an agent whose core decision-making relies on a large language model, augmented with tools (search, APIs, internal databases) to act and stay current. IBM explains that tool calling helps move beyond the limits of an "isolated" LLM: it can access up-to-date information, optimise workflows and create sub-tasks automatically (source). It is mainly used to execute complex workflows rather than only produce answers.

What do we mean by "agentic commerce", and what does it mean for brands?

Agentic commerce refers to buying journeys orchestrated end-to-end by agents, which become "customers" or delegates. A typical example is travel: transport, hotel, activities and payment are assembled automatically based on constraints and history, then confirmed via strong authentication. For brands, the implication is direct: make your offer readable, comparable and actionable (useful content, structured data, clear terms) and anticipate trust issues (identity, consent, payment).

What is the best AI agent for your B2B context?

There is no single "best" agent, because everything depends on the use case, available data, required integrations, compute budget and acceptable risk. IBM also notes there is no single standard architecture for building agents, and that different paradigms (such as ReAct or ReWOO) fit different problems (source). In B2B, the best choice is the agent that achieves a measurable goal with robust control (logs, permissions, approvals) at the most controlled cost.

What does "artificial intelligence agent" mean, and why does the term appear so often?

"Artificial intelligence agent" is simply the most common English expression for an AI agent. It appears frequently because much of the research and industry work on agentic architectures (planning, tool calling, multi-agent) is published in English. The term emphasises agency: a system that pursues a goal and acts, rather than a model that generates an answer.

To go deeper and follow our analysis on the topic, visit the Incremys Blog.