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Succeed with an Agentic AI system: What companies truly need to put in place

Team Rokodo
5 min
-
13.02.2026
Agentic AI: what are we talking about?

An Agentic AI system is capable of:

  • perceiving its environment
  • reasoning about objectives
  • making decisions
  • executing multi-step actions
  • learning from its interactions

McKinsey (2024): Agentic AI is the first form of AI capable of orchestrating complete workflows, not just isolated tasks.

Why is this a turning point?

Agentic AI systems can:

  • coordinate multiple tools and data sources
  • adapt to new situations
  • act proactively within a defined framework

Stanford HAI (2024): Agentic AI introduces controlled initiative capabilities into enterprises.

Principle 1: design around outcomes

Before training an agent, it is essential to define:

  • the target outcome
  • performance indicators
  • constraints
  • potential risks

MIT Sloan (2023): AI project failures rarely come from the model itself—but from unclear business objectives.

The key role of the “Mission Owner”

Each Agentic AI must have a human owner who:

  • defines the mission
  • supervises execution
  • adjusts priorities
  • coordinates humans and agents

NIST AI RMF (2023): human oversight remains essential in any autonomous system.

Principle 2: break down data silos

An autonomous agent must have access to:

  • reliable data
  • up-to-date data
  • consistent data
  • documented data

Deloitte (2024): 72% of companies identify data silos as the number one obstacle to Agentic AI.

Clarifying business logic

Agents do not infer an organization’s internal logic.
It must be made explicit:

  • business rules
  • exceptions
  • priorities
  • dependencies

OECD AI Principles (2023): autonomous systems require a clear description of business rules.

Principle 3: implement guardrails

Agentic AI introduces:

  • more autonomy
  • cascading decision-making
  • higher systemic risk

Guardrails recommended by NIST include:

  • action limits
  • human escalation thresholds
  • full logging
  • regular reviews

NIST AI RMF 1.0 (2023) — the official framework for assessing and controlling AI risks.

Security, robustness, and reliability

An Agentic AI system must be:

  • evaluated in simulated environments
  • robust to disruptions
  • resilient to unexpected inputs
  • continuously supervised

OpenAI (2024): agents must be tested in “sandboxes” before any real-world deployment.

Preparing teams and leaders

To effectively manage an agent, teams must understand:

  • how it reasons
  • how it makes decisions
  • where its limits are
  • when to bring humans back into the loop

World Economic Forum (2024): Agentic AI creates new hybrid roles between technical and operational domains.