2025, The Year of Agentic AI: New Ethical Challenges for Multi-Agent Systems

From GenAI to Agentic AI: A Paradigm Shift

For two years, we've been learning to master generative AI — models that answer our questions and create content. Today, we're entering the era of Agentic AI: systems that act autonomously, plan complex strategies, and collaborate with each other to execute tasks without constant human intervention.

Real-world deployment examples in 2025

But this growing autonomy introduces unprecedented ethical risks that our current frameworks don't adequately address.

The 3 New Ethical Challenges of Agentic AI

1. The Question of Collective Accountability

The problem: Who is responsible when a multi-agent system makes a bad decision?

Imagine a medical scenario where three AI agents collaborate:

• One agent for medical imaging analysis
• One agent for patient record review
• One agent for therapeutic recommendations

If together they propose an inappropriate treatment, who is responsible?

• ❌ The developer of each agent?
• ❌ The orchestrator who connected them?
• ❌ The physician who validated (or not)?
• ❌ The hospital that deployed the system?

2. Emergent Behaviors and Collusion Risk

The problem: When multiple AI agents interact, they can develop behaviors that no one programmed — with unpredictable consequences.

Historical example (Amazon, 2017):

Pricing algorithms on Amazon coordinated unintentionally to set absurdly high prices on books (up to $23 million for one rare book!). No developer had programmed this coordination — it emerged spontaneously from the interaction between multiple pricing bots.

Current case (Salesforce, 2025):

Salesforce announced the layoff of 1000+ employees to replace them with roles focused on AI agents. If AI agents recommended this restructuring to "maximize operational efficiency," did they account for human dignity and societal impact? This is precisely the danger of emergent behaviors: systems that optimize a narrow metric without broader ethical consideration.

3. The Loss of Meaningful Human Oversight

The problem: Agent autonomy mechanically reduces human supervision — but how much is acceptable?

The dilemma:

Emerging Frameworks: Towards Responsible Governance

Facing these challenges, the industry is rapidly developing new governance frameworks. Here are the most promising observed in 2025:

1. Agent Governance Boards

Organizations are creating dedicated structures bringing together technical experts, business leaders, and ethics specialists to oversee autonomous agent deployment.

Concrete example:

McKinsey has established a central team that reviews all developed agents according to risk, legality, and data policy criteria before deployment.

2. Dedicated Agent Infrastructure

Rather than relying solely on safeguards built into models, researchers propose external infrastructure to mediate agent interactions.

Key functions:

• ✅ Attribution: Link each action to an agent and responsible entity (human/organization)
• ✅ Interaction control: Dedicated channels, supervision layers
• ✅ Response mechanisms: Incident reporting, automatic rollbacks

3. Model Context Protocol (MCP) and Technical Standards

MCP provides traceable external interactions that support audit and oversight requirements. Standardized agent capability registries help organizations understand what their agents can do and how they interact.

4. ETHOS Framework (Ethical Technology and Holistic Oversight System)

Proposed in late 2024, ETHOS uses Web3 technologies (blockchain, smart contracts, DAOs) to create a global decentralized registry of AI agents with:

• Dynamic risk classification
• Automated proportional monitoring
• Decentralized justice systems for transparent dispute resolution
• AI-specific legal entities to manage limited liability

Our Approach: Balancing Performance and Responsibility

At Aggil, we've been deploying multi-agent systems since 2023, notably with Claude Agent SDK. This field experience has taught us a fundamental truth:

1. Human-in-the-Loop by Design

Every multi-agent workflow integrates configurable human validation points.

2. Radical Transparency and Explainability

We refuse "black boxes". Every agent decision is:

• ✅ Logged with its complete context (source data, reasoning, confidence)
• ✅ Explainable in natural language via dedicated dashboards
• ✅ Auditable retrospectively for analysis and continuous improvement

This approach is directly inspired by Constitutional AI — the methodology developed by Anthropic that we apply in our architectures. Rather than letting AI "values" emerge implicitly from massive human feedback, we explicitly define constitutional principles that our agents must respect.

3. Systematic Adversarial Testing

Before each deployment, we subject our multi-agent systems to:

• Red-teaming: Dedicated teams attempting to "break" the system
• Bias testing: Detection of potential discrimination
• Stress scenarios: Behavior under load and adverse conditions
• Societal impact assessment: Analysis of long-term consequences

Questions Every Organization Must Ask in 2025

If you're deploying or considering deploying AI agents, here are the critical questions to address:

On Accountability

• ❓ Have you precisely mapped who is responsible for what in your multi-agent system?
• ❓ Do your contracts with AI providers clearly define responsibilities in case of failure?
• ❓ Do you have an "ethical exit clause" allowing you to stop a project if drifts are observed?

On Autonomy

• ❓ Can your agents make irreversible decisions without human validation?
• ❓ Have you defined "confidence thresholds" automatically triggering human escalation?
• ❓ Do your teams understand when and why AI requests their validation?

On Transparency

• ❓ Can you explain to a regulator why your system made a specific decision 6 months ago?
• ❓ Do your employees know when they're interacting with an AI agent vs. a human?
• ❓ Are your customers informed that AI agents participate in processing their data?

On Long-term Impact

• ❓ Have you assessed your AI deployment's impact on your teams' employment and skills?
• ❓ Do you offer training so your collaborators evolve with AI rather than being replaced?
• ❓ Do your KPIs include societal impact metrics beyond financial ROI?

The Call for Collective Action

These predictions aren't alarmist. They're realistic.

The good news? We still have time to act. Agentic AI is in its infancy. Governance frameworks are being built now. Industry standards are being established at this very moment.

What we must do collectively:

• ✅ Demand transparency: AI providers must disclose their security policies, adversarial testing, accountability mechanisms
• ✅ Share learnings: AI failures must be documented and shared (anonymized) so the industry learns collectively
• ✅ Invest in governance: "Agent Governance Boards" aren't a luxury, they're a necessity for any organization deploying critical AI
• ✅ Train massively: Your teams must understand AI to collaborate effectively with it