Principal-Agent Relationship: What It Is, How It Works, and New Developments

Key takeaways
* A principal appoints an agent to act on their behalf and in their best interest (e.g., shareholders and executives, investors and fund managers).
* The relationship is fiduciary: agents owe duties of care and loyalty and must avoid conflicts of interest.
* The principal-agent problem arises when incentives diverge and information is asymmetric, enabling agents to act in their own interest.
* New technologies—especially AI, machine learning (ML), blockchain, and decentralized finance (DeFi)—are changing how agency problems appear and how they can be addressed.

What the principal-agent relationship is

A principal-agent relationship is a legal and fiduciary arrangement in which one party (the principal) delegates authority to another party (the agent) to perform tasks or make decisions on the principal’s behalf. It can be created explicitly by contract or implicitly through actions and accepted responsibilities.

Common examples:
* Shareholders (principals) and corporate executives (agents)
* Investors (principals) and fund managers or financial advisors (agents)
* Clients and attorneys, contractors, or service providers

How the relationship works

Agents are expected to carry out instructions with appropriate skill and care, and to prioritize the principal’s interests. The principal provides authority and (typically) guidance; the agent executes tasks within that scope. The relationship exists in many forms—individuals, corporations, nonprofits, and government entities can all act as principals.

Fiduciary duties

When a principal-agent relationship exists, the agent generally has fiduciary duties, including:
* Duty of care: perform tasks competently and reasonably.
* Duty of loyalty: avoid conflicts of interest and not put personal gain ahead of the principal’s interests.
* Duty to follow lawful instructions and to act in good faith.

Breach of these duties can lead to legal liability and damages.

The principal-agent problem

The principal-agent problem occurs when:
1. Incentives are not perfectly aligned between principal and agent.
2. The agent has more or better information than the principal (information asymmetry), and the principal cannot fully monitor the agent’s actions.

When both conditions hold, agents may take actions that benefit themselves at the expense of the principal—examples include excessive risk-taking, short-termist decisions, and misuse of resources. The issue is central to corporate governance, financial markets, and broader economic theory.

Origins and theory

Agency theory developed in the mid-20th century as economists shifted focus from industry-wide models to firm-level behavior and internal incentives. Formal models and empirical work clarified how agency costs arise and suggested mechanisms to manage them.

Addressing principal-agent problems

Typical approaches aim to align incentives or reduce asymmetric information:
* Performance-based compensation tied to long-term outcomes
* Monitoring, reporting, and transparency requirements
* Strong governance and board oversight
* Regulatory and legal enforcement of fiduciary duties
* Training and ethical standards for agents

These measures reduce—but do not eliminate—agency risk. They must adapt as technologies and business models evolve.

New developments reshaping agency relationships

AI and machine learning

ML and AI introduce novel agency challenges:
* Opacity: many ML models (especially deep learning) operate as “black boxes,” making it hard for principals or even developers to fully explain decisions.
* New information asymmetries: agents may rely on or control automated systems whose logic is not transparent to principals.
* Amplified incentives: algorithmic strategies can scale agent behavior rapidly, amplifying potential misalignment.

Applications affected include automated trading, robo-advisors, credit scoring, and fraud detection. Traditional contractual or monitoring solutions can be less effective when decision rules are opaque or adapt in complex ways.

Blockchain and DeFi

Blockchain technologies introduce both potential remedies and new forms of agency risk:
* Smart contracts: self-executing code can reduce the need for some intermediaries and limit discretionary agent behavior.
* Decentralized autonomous organizations (DAOs): community governance models can realign incentives among stakeholders but raise questions about accountability, legal status, and oversight.

These tools can reduce certain agency costs but also create implementation and governance challenges.

Regulatory and governance responses

Regulators and institutions are responding with new priorities:
* Explainable AI: pushing for models and processes that can be interpreted and justified.
* AI governance frameworks: internal controls, testing, and oversight tailored to algorithmic systems.
* Ethical AI principles: addressing bias, fairness, and societal impacts of automated decisions.

These responses aim to restore transparency and accountability where human oversight is limited.

Changing compensation and incentive design

Compensation practices are evolving to better align agent behavior with longer-term and broader stakeholder goals:
* Longer-term incentive plans and deferred compensation
* ESG-linked compensation to incorporate environmental, social, and governance objectives
* Team-based incentives to reflect collaborative work and reduce perverse individual incentives

FAQs

What is the principal-agent problem?
A misalignment of priorities between a principal and an agent combined with information asymmetry, which can enable the agent to act in ways that don’t serve the principal’s best interest.

Why hire an agent?
Principals delegate to agents because agents can save time, provide specialized skills, or manage tasks at scale that the principal cannot perform effectively.

What happens if an agent breaches their fiduciary duty?
An agent who breaches fiduciary duties can face legal consequences, including monetary damages, injunctions, or contract termination, depending on the terms and applicable law.

How can principals reduce agency risk?
Clear contracts, aligned compensation, monitoring and reporting, governance structures, and regulatory compliance reduce risk. With AI or automated systems, principals should also demand explainability, robust testing, and effective governance of algorithms.

Bottom line

Principal-agent relationships are foundational to modern finance, business, and public institutions. They enable specialization and scale but introduce the risk that agents will pursue their own interests. Traditional tools—contracts, incentives, monitoring, and law—remain important, but new technologies such as AI, ML, and blockchain are reshaping how agency problems appear and how they must be managed. Effective oversight now requires combining classic governance with technical controls, transparency, and evolving regulatory frameworks.