October 2, 2026

What Is an AI Agent and How to Create One?

Table of contents

Key Takeaways

An AI agent is not an AI model. It is an assembly: a reasoning engine, memory, planning, and the ability to act on your systems.

What changes: generative AI produces text. An AI agent triggers real actions.
What the science says: the architecture is stabilised, but real-world success rates remain modest — 42% at best.
The success condition: a structured document base, controlled permissions, calibrated supervision.

What Is an AI Agent?

An artificial intelligence agent — also called an intelligent agent — is a computational entity situated in an environment, capable of observing it, deliberating through a reasoning engine, and acting autonomously towards a defined goal. It combines decision-making capabilities, natural language processing and tool integration to complete tasks in context (Cheng et al., 2026; Li et al., 2024).

It is not a model. The literature is categorical: “an autonomous AI agent cannot be reduced to a foundation model”.

The Four Defining Properties of an AI Agent

(Cheng et al., 2026; Botti, 2025; Molinari & Ciravegna, 2026)

Property	Definition	Everyday Analogy
Autonomy	Operating over long sequences without step-by-step guidance	A robotic vacuum that maps and cleans on its own
Reactivity	Perceiving changes and responding in time	A GPS that recalculates the moment an accident is reported
Proactivity	Breaking a broad goal into sub-objectives	An assistant that, to “schedule a meeting”, checks calendars, books the room, and sends invitations
Social capability	Cooperating and communicating with humans or other agents	Apps coordinating to deliver a package

Combined, these properties produce continuous autonomy and adaptation: when faced with the unexpected, the agent re-evaluates and adjusts its plan. This autonomous decision-making distinguishes it from a conventional program — and makes its supervision essential.

From Bots to Agents: Three Levels of Autonomy

A bot executes. An assistant advises. An AI agent decides and acts. The deciding criterion: can it modify something in your systems without being asked to at every step?

	Bot	AI Assistant	AI Agent
Functioning	Reactive — fixed script	Reactive — responds to prompts	Proactive — takes initiative
Autonomy	Low	Medium	High
Natural language	Keywords, decision trees	Native	Native, with intent decomposition
Learning	None	Partial	Continuous
Example	Automated FAQ, voice server	Copilot, ChatGPT, advanced conversational agents	Document agent, process orchestration agent

A Debate Worth Knowing

Botti (2025) notes that “agentic AI” revisits concepts formalised as far back as the 1990s. Large language models bring unprecedented flexibility in interpreting natural language, but “lack deterministic guarantees of termination and logical consistency”.

For a decision-maker: the novelty lies in ease of use, not in reliability.

What Is the Role of an AI Agent?

Five deployment domains dominate the 2024–2026 literature: software engineering, scientific research, financial services and enterprise workflows, social simulation, and embodied agents.

Agent family	What they do	Maturity
Code agents	Fix bugs, write tests, review code	High — best-documented use case
Customer support agents	Handle a request end to end: diagnosis, action, response	High
Document agents	Extract, classify, cross-check and control incoming documents	High on scoped perimeter
Data analysis agents	Query databases, produce summaries, detect anomalies	Medium
Process orchestration agents	Coordinate multiple business applications across a complete workflow	Medium — depends on connectors
Research agents	Compile, compare and synthesise sources	Medium

What AI Agents Look Like in Production Today

An AI agent’s role is not to replace judgement: it absorbs the preparation work that precedes it. The real gain: automating complex but repetitive tasks that consume expert time without drawing on expert insight.

How Does an AI Agent Work?

Four interconnected modules.

1. Profiling — “who am I?” Business role, perimeter, behavioural constraints.

2. Memory — “what do I know?” Three layers: working memory, episodic and semantic memory (RAG), and procedural memory. An agent’s long-term memory is your document base — your contracts, procedures, files. In some systems, this memory supports adaptive learning through feedback and iterative refinement, without requiring continuous retraining of the underlying model.

3. Reasoning and planning — “how do I get there?” In-context planning, external symbolic planning, and self-reflection loops (ReAct, Reflexion).

4. Action and tools — “I execute” API calls, script execution, database querying, interface manipulation.

🎯

Profiling

Role, perimeter, behavioural constraints

⚙️

Reasoning & Planning

Decomposition, chains of thought, self-reflection

🤖AI Agent

🧠

Memory

Working, episodic, semantic, procedural

🔧

Action & Tools

APIs, scripts, databases, interfaces

The Scientific Formula, in Plain Language

Cheng et al. (2026) formalise the agent as a quintuple: V = (L, O, M, A, R)

L	O	M	A	R
Reasoning engine	Assigned mission	What it knows and retains	Available tools	Self-correction capability

Remove any single element: you no longer have an agent, you have a chatbot.

What Are the Different Types of AI Agents?

Single-Agent or Multi-Agent Systems?

Multi-agent systems distribute work among specialised agents according to three models:

Model	Principle	Analogy
Hierarchical	A planner decomposes and delegates	A project manager distributing work
Peer-to-peer	Agents debate until convergence	A committee deliberating before deciding
By functional roles	Each agent embodies a specialism	A cross-functional team

Orchestrator

↓

Agent A

Agent B

Agent C

Hierarchical

A central planner decomposes and delegates.

Project manager → team

⇄

Peer-to-peer

All agents debate and converge with no fixed leader.

Committee deliberating

📋 Analyst

✍️ Writer

🔍 Reviewer

📊 Report

By Functional Roles

Each agent embodies a specialism.

Cross-functional team

The Five Levels of Autonomy

Level	Nature of agent	Human supervision
L1 — Partial	Deterministic tool-agent, isolated instructions	Full
L2 — Conditional	Consultant agent, short context	Systematic
L3 — High	Collaborative agent, complete loops	Sampling-based
L4 — Near-total	Reflective agent, redefines sub-goals	Exceptional
L5 — Total	Autonomous over indefinite horizon	Theoretical

How to Create and Deploy an AI Agent?

Step 1 — Define a Verifiable Objective

Write an objective a third party could verify: ❌ “Automate invoice management” vs ✅ “Extract the number, pre-tax amount, due date and supplier from every PDF invoice, then flag any duplicate from the past 90 days”.

Step 2 — Structure Memory Before Intelligence

Connect your agent to a disorganised base, and the result won’t be approximate. It will be wrong, with confidence.

Level	What the agent does	When to use it
Instant Memory	Reads one or a few documents on demand, no prior indexing	One-off use: summarise a contract, extract clauses, compare two quotes
Semantic Memory	Searches a pre-indexed corpus via semantic search	Large, stable corpus: procedures, internal FAQ, helpdesk
Entity Memory	Extracts and structures business entities (amounts, deadlines, clauses, stakeholders)	Zero-approximation decisions: tenders, compliance, contract monitoring

Want to test Efalia’s AI DMS?

Discover how to structure your document memory before deploying an agent.

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Step 3 — Standardise Connections via Open Protocols

Our article details how to transform your enterprise with a document management system truly compatible with agentic AI.

Step 4 — Choose a Platform and Calibrate Permissions

Efalia integrates with DUST, Microsoft Copilot Studio, Google Agentspace and IBM watsonx Orchestrate via MCP. User permissions defined in the DMS are propagated to the agent.

Step 5 — Test the Agent Before Trusting It

Model	Success rate (Pass@1)
GPT-5	42.1%
Claude-4 Sonnet Thinking	37.8%
Kimi-K2	20.1%

Risks and Key Considerations

The Risk Shift

An agent hallucination “translates directly into destructive, persistent and irreversible material actions in databases”. This is an integrity problem for your information system. Human-in-the-loop oversight, data privacy and ethical considerations are essential when an agent can access enterprise systems or modify data.

The Five Families of Hallucinations

Family	Manifestation
Reasoning	Misinterpretation of the goal, faulty decomposition
Execution	Invoking non-existent tools, fabricated parameters
Perception	Visual anchoring errors, contradictory sensory fusion
Memory	Truncated vector retrieval, distorted interaction history
Communication	False-message propagation in multi-agent networks, cascade effect

Security: AI Is Not Yet Particularly Safe

Average security score below 60%, some under 20%. Tool-use rule compliance: 38.5%.
Vulnerable to prompt injection — six entry points identified.
ARGUS architecture: successful attacks from 28.8% to 3.8%, utility maintained at 87.5%.

Blocking Alone Is Not Enough

A useful safeguard explains why the action is refused, proposes an alternative, and escalates to a human when no path fits.

The Sovereignty Question

Open models reach 60–95% of GPT-4 performance on targeted domains. Our article on how AI improves document management in the enterprise.

Conclusion

AI agents: documented systems, stabilised architecture, precisely measured limitations.
The agent is not the model. Memory — your structured documents — determines result quality.
Maximum autonomy is not the goal. L2 and L3 cover most business needs.
Security is designed upstream: propagated permissions, causal traceability, guided replanning.

👉 Discover our AI DMS platform at efalia.com/start-ia.

Frequently Asked Questions

What is the difference between an AI agent and an AI assistant?

An AI assistant responds to your prompts. An AI agent pursues a goal and acts on its own initiative — chaining steps, calling tools and modifying data without validation at every move.

What is the difference between an AI agent and an LLM?

The LLM produces text; the agent uses it as a reasoning engine. Around it: role profile, structured memory, planning, tools. V = (L, O, M, A, R).

What are the most widespread AI agent examples?

Code agents, customer support agents, document agents.

Can an AI agent work without structured documents?

Technically yes, but poorly controlled documents directly bias the agent’s reasoning (Knowledge Poisoning, He et al., 2025).

What autonomy level for a first project?

L2 or L3: consultant agent with human validation, or collaborative agent with sampling-based supervision.

Do you need technical skills to create an AI agent?

No. The key skill: formulating a semantically verifiable objective.

How do you know if your agent is working correctly?

Do not ask an AI to evaluate it: Froger et al. (2026) measured an accuracy of 0.52 for this method, compared with 0.99 when directly checking the changes produced. Audit the actual changes in your databases, files and tickets. Not the apparent quality of the answers.

GDPR compatibility?

Open models reach 60–95% of proprietary performance without data leaving your infrastructure.

Sources

Literature review: 2026 State of the Art on Artificial Intelligence Agents, 17 September 2026. Corpus: 220 records → 148 after deduplication → 21 retained, including 15 full-text studies analyzed.

Botti, V. (2025). Agentic AI and Multiagentic: Are We Reinventing the Wheel? arXiv:2506.01463
Cheng, Y., et al. (2026). Exploring LLM-Based Intelligent Agents. WIREs DMKD. doi:10.1002/widm.70111
Chowa, S. S., et al. (2026). From language to action. Artificial Intelligence Review. doi:10.1007/s10462-025-11471-9
Dantas, P., et al. (2026). Toward Safe LLM Agents. arXiv:2608.14590
Froger, R., Andrews, P., & Bettini, M. (2026). GAIA-2. ICLR 2026
He, F., et al. (2025). The Emerged Security and Privacy of LLM Agent. ACM CSUR. doi:10.1145/3773080
Li, X., et al. (2024). A survey on LLM-based multi-agent systems. Vicinagearth. doi:10.1007/s44336-024-00009-2
Lin, X., et al. (2025). LLM-Based Agents Suffer from Hallucinations. arXiv:2509.18970
Mohamed, N., et al. (2026). A Systematic Survey of LLM-Based Agentic AI Frameworks. JSSA. doi:10.66279/y29vex64
Molinari, G., & Ciravegna, F. (2026). Towards Pervasive Distributed Agentic Generative AI. ACM CSUR. doi:10.1145/3821566
Sarkar, A., & Sarkar, S. (2025). Survey of LLM Agent Communication with MCP. arXiv:2506.05364
Su, H., et al. (2026). Autonomy-Induced Security Risks in Large Model-Based Agents. IEEE TDSC. doi:10.1109/TDSC.2026.11498611
Weng, S., et al. (2026). ARGUS. arXiv:2605.03378
Xu, H., et al. (2026). The Evolution of Tool Use in LLM Agents. arXiv:2603.22862
Yang, Y., et al. (2026). AgentNet. NeurIPS

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