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Enterprise AI agents: deploy, industrialise,
govern in production.

An enterprise AI agent is a system that pursues a business goal autonomously: it reasons, uses tools, acts on your systems and stays under human oversight. The real challenge isn't prototyping one — it's getting it into production and governing it. That's what Koneetiv, a pure-play Anthropic partner, does.

Scope my first deploymentSee the definition →
87%
of AI projects never reach production (McKinsey 2025)
12×
more projects in production with an AI governance framework (BCG 2026)
16
deployable agent modules, from scoping to production
LOOP™
human-in-the-loop governance in production
87%of AI projects never reach production (McKinsey 2025)
12×more projects in production with an AI governance framework (BCG 2026)
16deployable agent modules, from scoping to production
LOOP™human-in-the-loop governance in production
87%of AI projects never reach production (McKinsey 2025)
12×more projects in production with an AI governance framework (BCG 2026)
16deployable agent modules, from scoping to production
LOOP™human-in-the-loop governance in production
Definition

An AI agent
is neither a chatbot, nor a rigid automation.

A chatbot answers. An RPA script runs a fixed rule. An AI agent, however, receives a goal, breaks it down, picks the right tools, checks its own work and produces a result. Four properties set it apart — and explain why it's deployed differently.

Goal
Goal-driven autonomy
You hand it a result to reach, not a list of steps. It plans, executes and self-corrects until the goal is met.
Tools
Action on your systems
Through connectors — the MCP protocol is the reference — the agent calls your systems (CRM, ERP, business databases, APIs) to act, not just answer.
Context
Business data & memory
It draws on your documents and data at the right moment, and keeps track across long, multi-step tasks.
Control
Human oversight
Validation checkpoints, traceability, trust zones: humans stay in control. That's the whole point of governance — the LOOP™ methodology at Koneetiv.
Anatomy

The 5 building blocks of an AI agent
you can trust in production.

An agent that holds up in the enterprise isn't a clever prompt. It's an assembly of five layers — from model to oversight — that must be industrialised.

1The reasoning modelBlock 01

The engine that understands the goal, plans and decides. Koneetiv builds on Anthropic's Claude models, among the most reliable on the market for agentic use.

2Tools & connectorsBlock 02

The layer that links the agent to your systems and data. The MCP (Model Context Protocol) has become the open standard for this.

3Context & knowledgeBlock 03

Documents, knowledge bases and enterprise data injected at the right moment to ground the agent in your business reality.

4OrchestrationBlock 04

Chaining steps, coordinating multiple agents and handling errors. The move from a hacked-together POC to an industrial agent.

5GovernanceBlock 05

Continuous oversight, traceability, trust zones and ISO 42001 / EU AI Act compliance. The layer that makes the agent deployable — and auditable. At Koneetiv: the LOOP™ methodology.

Block 01
The reasoning model
The engine that understands the goal, plans and decides. Koneetiv builds on Anthropic's Claude models, among the most reliable on the market for agentic use.
Claude Opus · Sonnet · Haiku · extended reasoning
Block 02
Tools & connectors
The layer that links the agent to your systems and data. The MCP (Model Context Protocol) has become the open standard for this.
MCP · business APIs · CRM / ERP · search · code execution
Block 03
Context & knowledge
Documents, knowledge bases and enterprise data injected at the right moment to ground the agent in your business reality.
RAG · document stores · task memory · real-time data
Block 04
Orchestration
Chaining steps, coordinating multiple agents and handling errors. The move from a hacked-together POC to an industrial agent.
Workflows · multi-agent · error recovery · guardrails
Block 05
Governance
Continuous oversight, traceability, trust zones and ISO 42001 / EU AI Act compliance. The layer that makes the agent deployable — and auditable. At Koneetiv: the LOOP™ methodology.
Observability · human validation · audit trail · compliance
The production wall

Why 87% of AI projects
never reach production.

Prototyping an agent takes a few days. Getting it into production, securing it and running it over time — that's where most projects stall. Four obstacles keep coming back.

Method
The POC → production wall
A POC that shines in a demo isn't an industrial agent. Without a scaling method, pilots pile up without ever generating ROI.
Accountability
The governance gap
When an agent makes a consequential decision, who's accountable? 69% of executives don't have the answer (BCG 2026). Without a framework, production stays blocked by risk.
Economics
The missing ROI
Token, integration and oversight costs poorly anticipated: a profitable agent is designed at the scoping stage, not after deployment.
Compliance
Security & compliance
Sensitive data, EU AI Act, ISO 42001: a non-compliant agent won't clear the security committee. Compliance is built in by design, not bolted on.
Frequently asked questions

Deploying AI agents in the enterprise: the essentials.

What is an enterprise AI agent?
An enterprise AI agent is an artificial intelligence system able to pursue a business goal autonomously: it reasons, uses tools to act on systems (CRM, ERP, APIs), draws on your data and stays under human oversight. Unlike a chatbot, which only answers, an agent carries out tasks end to end.
What's the difference between an AI agent and automation (RPA)?
Classic automation (RPA) runs fixed rules, step by step: the moment a case falls outside the planned scenario, it fails. An AI agent reasons in the face of the unexpected — it adapts, chooses its actions and handles unscripted situations. RPA follows a path; the agent decides the path. The two are complementary.
Why do 87% of AI projects never reach production?
According to McKinsey (2025), most generative AI projects stay at the POC stage. The main causes: no scaling method, unclear governance and accountability, poorly scoped ROI, and compliance requirements (EU AI Act, ISO 42001) handled too late. Conversely, organisations with an AI governance framework deploy 12 times more projects into production (BCG 2026).
How do you deploy AI agents in the enterprise?
Deployment rests on five building blocks — reasoning model, tools and connectors (MCP), business context, orchestration and governance — then on a progressive approach: scope the use case, run a measured pilot, scale up. Koneetiv structures this journey with the Claude Ignite methodology and LOOP™ governance, from audit to production.
How much does deploying an AI agent cost?
Cost depends on scope: use-case complexity, number of connected systems, processing volume and the level of oversight required. Beyond model (token) cost, you have to factor in SI integration, compliance and production monitoring. A profitable agent is sized at the scoping stage: Koneetiv quantifies ROI case by case before any commitment.
How do you govern AI agents in production?
Governing an agent in production means ensuring continuous human oversight, decision traceability, trust zones (from shadow to autonomous mode) and ISO 42001 / EU AI Act compliance. Koneetiv's LOOP™ methodology (Living Oversight & Operations Protocol) frames this oversight throughout the agent's lifecycle.
Go further

From concept
to an agent in production.

You know what an AI agent is and what it takes to deploy one. Here's where to go next.

Technology
Claude agent
The agent engine we deploy, in detail.
Methodology
Claude Ignite
From scoping to production for your agents.
Governance
LOOP™ governance
Supervise and secure your agents in production.
Definition
Chatbot or AI agent?
The complete guide to telling them apart.

Ready to move
from POC to production?

We identify your best use case, scope a measurable pilot and map the path to production — with governance from day one.

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