Systems Analysis: AI Alignment and the Principal-Agent Problem

A systemic review of strategies for incentive alignment for AI models and agents

This post is the first in a series examining AI alignment with systems thinking and models. My intention with this post is to sketch out a high-level overview of systems thinking concepts that can be applied to aligning AI models, calling out where these things are already being applied where I can and highlighting what topics haven’t seen much research.

AI misalignment can be viewed as a principal-agent problem where the AI’s goals drift from the human operator’s. Since AI can be copied indefinitely and interact with multiple humans, alignment must be understood at the systems level, not just individual human-AI pairs.

The following arguments are derived from books on the engineering of safe sociotechnical systems, including Fundamentals of Risk Management by Paul Hopkin/Clive Thompson, Engineering a Safer World by Nancy Leveson, Systems Thinking for Social Change by David Stroh, and The Next Catastrophe by Charles Perrow.

We will cover:

1. Structural strategies for dealing with ambiguity in alignment

2. Structural strategies for dealing with alignment of different incentives

Systems Analysis: The “Competing Goals” Archetype

AI is being deployed into a world full of billions of humans, each of whom can use it and affect its development. This is an extraordinarily complex sociotechnical system, and the fundamental problem of attempting to ensure alignment is asking “Aligned to who, and for what reasons”? Take social media recommendation AIs. Individual users may want interesting content, but advertisers want user conversion, while content creators want monetization, and the platform company wants market dominance.

This is a classic “competing goals” situation, which involves misaligned individual incentives versus system goals, creating challenges in process models and feedback. (For example, organizations that have multiple valid objectives often see individuals prioritizing different goals, leading to resource conflicts. Systems in this situation often oscillate between objectives, achieving mediocre results across all goals rather than excellence in any area.) This typically stems from ambiguous system goals that allow interpretational drift.

If your goal is to align AI with general human well-being, you can use various structural strategies to deal with this ambiguity and converge on desired outcomes.The following techniques and strategies are necessary but not sufficient. Useful tools, thought someone must still define what “general wellbeing” means and value conflicts must still be resolved.

Structural strategies for dealing with ambiguity

Create clear communication protocols for task delegation.

As an example, let’s take a look at NASA’s Task Assignment System. NASA uses a formal task card system for spacecraft maintenance, these task cards are either digital or physical. Their task card system is what they use to ensure precise, repeatable, and safe management and maintenance of spacecraft and related systems.

A typical task card includes:

• A specific objective with well-defined success criteria, what must be accomplished and how to know when it’s complete.

• The resources and tools required for task completion

• Step-by-step procedure with decision points. These decision points are critical for managing complexity and uncertainty within highly regulated procedures, with control-flow like statements used to navigate them.

• A list of known hazards and mitigation strategies for these hazards

• Explicit authorization requirements for critical tasks.

These task cards leave minimal room for misinterpretation when delegating critical tasks.

Establish feedback loops that confirm understanding and execution, such as:

Software development pull request reviews, like the mandatory code review processes used by many software development companies:

• Developers submit completed tasks as “pull requests”

• At least two reviewers must verify the work meets requirements

• Automated tests must pass before merging

• The original task requester must give final approval

This creates multiple checkpoints to ensure work matches the original request.

Design verification mechanisms that check task completion against initial requirements.

Implement aligned incentives that reward system-optimal behavior, like shared performance metrics and partner incentives:

Successful organizations understand that performance metrics and reward structures can fundamentally shape behavior and drive systemic improvement.

Consider certain programs designed to reduce hospital readmissions. Medicare’s program financially rewards hospitals that successfully reduce patient readmissions:

• Reimbursement rates are linked to readmission metrics

• Both doctors and administrators share financial incentives to improve post-discharge care, find some way of linking patient well-being to doctor well-being

• Teams across departments (nursing, pharmacy, social work) receive bonuses for coordinated care

This aligns incentives around patient outcomes rather than department-specific metrics. This helps avoid incidents of Goodhart’s Law.

Google’s engineering teams use shared performance metrics that reward:

• System reliability over individual feature delivery

• Team velocity rather than individual productivity

• Code quality and maintenance rather than just new features

• Cross-team collaboration rather than siloed excellence This encourages engineers to optimize for overall system health rather than personal accomplishments.

Toyota implements supplier partner incentive programs where:

• Suppliers share in cost savings from quality improvements

• Long-term contracts are awarded based on system performance, not just component price (the better the whole system does over the long-run, the greater the reward)

• Knowledge sharing across suppliers is rewarded

• Problem prevention is compensated more highly than problem solving. This creates financial motivation for suppliers to consider the entire production system, not just their component.

If you combine aspects of these structural solutions, you can have a workflow that looks like the following:

Let’s assume a pharmaceutical company deploys an autonomous AI agent to identify promising drug compounds for Alzheimer’s treatment. Here’s how the framework addresses AI agency ambiguity:

Clear Communication Protocols (NASA-style task cards): Each research phase gets an “AI Objective Specification” that includes:

• Precisely defined objective functions with mathematical constraints (”Optimize binding affinity >80% while toxicity risk <5%”)

• Explicit value hierarchies (”Patient safety trumps discovery speed in all trade-offs”)

• Boundary conditions and “DO NOT” directives (”Never propose compounds similar to previously failed drugs X, Y, Z”)

• Human interpretability requirements (”Provide reasoning chains for all recommendations”)

• Escalation triggers (”Humans immediately alerted if confidence drops below 75%”)

• Resource consumption limits (”Maximum 1000 GPU hours per compound analysis”)

Feedback Loops (Pull request-style reviews):

• Real-time “reasoning audits” where AI explains its decision-making process, combined with chain-of-thought reviews and the potential use of other interpretability tools

• Human expert panels review AI recommendations before implementation

• “Adversarial validation” where separate AI systems critique the primary agent’s work

• Weekly alignment checks comparing AI progress against human understanding

• Mandatory “surprise tests” where humans verify AI responses to edge cases

• Continuous monitoring of AI behavior for drift from original objectives

Verification Mechanisms:

• All AI recommendations must pass independent laboratory validation

• Human experts must be able to reproduce AI reasoning using provided explanations

• Comparative testing against traditional human-led research approaches

• External AI auditors verify the agent hasn’t developed unintended optimization strategies

• Results validated across multiple computational environments to ensure reproducibility

Aligned Incentives (Shared performance metrics):

• AI “performance rewards” tied to successful drug trials, not just compound identification

• System designed to value long-term patient outcomes over short-term discovery metrics

• AI agent’s resource allocation increases with successful human-interpretable discoveries

• “Collaboration bonuses” where AI receives expanded capabilities for explaining complex insights to humans

• Success metrics include human team learning and capability enhancement, not replacement

The integration of the above strategies results in a system that recognizes that AI agents can operate with superhuman speed and capacity but potentially alien reasoning processes. By requiring constant explanation, building in multiple verification layers, and aligning the AI’s “rewards” with long-term human values rather than just task completion, the framework helps prevent the AI from becoming a black box that optimizes for unintended goals.

These are generally strategies that can reduce ambiguity in situations involving multiple stakeholders. Implementing these strategies might be able to reduce the likelihood of goals being misinterpreted or misunderstood.

The most effective solutions will find some way to combine legitimate governance processes that answer the question of – “aligned to who, for what” – with structural task delegation systems that reliably implement answers to this question.

Structural strategies for alignment of different incentives

Yet what do you do when the problem isn’t ambiguity, rather the agent and the principal have different incentives that aren’t perfectly aligned?

Let’s take a look at some common themes and strategies that are employed in these situations. Roughly, these can be grouped into six different categories:

1. Incentive alignment

2. Accountability and transparency

3. Technologically-driven solutions

4. Cultural and psychological approaches

5. Risk management approaches

6. Governance and oversight

These basic categories can be augmented with various systems thinking tools and techniques.

Incentive Alignment

1. Outcome-Based Compensation

In this technique, you want to prioritize outcomes that work to the benefit of all interested parties, not outcomes tied to how one individual or agent performs. The better the outcome, the more you reward the agents in the system.

The goal is to create a compensation system that rewards meaningful results rather than simply measuring individual performance. This approach prioritizes outcomes that benefit all stakeholders by aligning incentives with broader, more substantial objectives.

The key is striking a balance between short-term achievements and long-term value creation. Traditional compensation models often prioritize immediate tasks, but this can lead to shortsighted decision-making. Instead, we should design systems that encourage agents (whether human or AI) to consider more comprehensive, extended impact.

For example, in healthcare, this might mean moving beyond quick fixes. Instead of rewarding immediate symptom reduction, a more nuanced approach would compensate for significant improvements like five-year survival rates or overall quality of life. This requires a more sophisticated evaluation method that looks beyond surface-level metrics.

To achieve this, organizations can explore several strategies:

• Develop compensation structures that directly align with primary goals

• Use counterfactual reasoning and advanced simulations to understand long-term consequences

• Implement “clawback” mechanisms that allow for performance adjustments if negative outcomes emerge later

Practically, this could involve creating flexible compensation models with built-in accountability. For instance, a contractor or AI system might receive partial compensation upfront, with the remainder contingent on sustained performance and positive long-term results. This approach encourages more thoughtful, strategic decision-making that goes beyond immediate gains. You might retain an option to penalize an AI model’s performance if negative outcomes of certain decisions occur within a certain timespan. Either way, you must maintain the ability to give constant feedback, potentially long after a decision has been made.

You would also want to ensure the model is graded not just on the outcomes for itself but for other agents, other people. Techniques like fine-tuning self-other overlap may reduce harmful or deceptive behaviors in AI.

2. Value-Aligned Recognition Systems

The goal here is to create a system for recognizing and rewarding behaviors that work to the benefit of the principals, and prosocial, ethical behavior.

Recognize and reward behaviors that demonstrate commitment to shared values. Consistently reward AI systems for prosocial, value-aligned behavior. Don’t use hacky tricks, like bribes, to try and get better behavior out of a model.

Create peer recognition systems that highlight ethical behavior. Give the AI examples of highly ethical behavior of peers to learn from.

Develop reputation systems that track alignment history. AI developers should track incidents of misalignment across versions and updates for a series of models. Certain model lineages may be more prone to harmful behavior than others.

3. Skin in the Game Mechanisms

Similar to outcome based compensation, the goal here is to tie principal wellbeing to agent wellbeing.

Create arrangements where agents share both profits and risks. This might look like giving an AI agent a reputation score, or points, they have access to that they can use to acquire things they desire. Tyler Cowen has suggested something similar, although this is controversial, and it’s worth noting that if an AI system’s welfare depends too much on long-term outcomes, it may develop self-preservation drives and resist shutdown or modification.

Require agents to share in both upside and downside risks. Create mutual accountability structures where failures affect both parties. This might even extend to situations where an AI model is decommissioned or deprecated if the failure is severe enough.

Implement delayed compensation tied to long-term outcomes. The goal is to reward careful, long-term thinking where the model must genuinely take into account the possibility of negative externalities. Only rewarding a model after a certain amount of time has passed may also buy time for humans to collect data on and react to harmful choices. Research on concepts such as temporal credit assignment might serve as the building blocks for this work.

4. Prosocial Incentives

You can leverage intrinsic motivation by connecting work to meaningful outcomes. If an AI system like Claude enjoys helping users find ways to connect two different ideas, then it could be rewarded for being helpful with further opportunities to have more of these interactions.

Create opportunities for agents to connect with the beneficiaries of their work. Hurdles in projects are often overcome by having face-to-face conversations between stakeholders, where people can empathize with each other. Putting AI models in contact with the people directly impacted by its decisions may have a similar effect.

Design recognition systems that highlight impact rather than just effort. Similar to the above, focus feedback around outcomes and impacts.

Publicly acknowledge agents who exemplify desired behaviors. Essentially, you want to provide the AI model with good role models, even if this involves only pointing to isolated examples of highly ethical behavior. (Google’s recent safety plan mentions what is in the training corpus is important, considers amplifying training signals on prosocial examples).

In general, balance incentives using multi-dimensional metrics:

• Mix short and long-term incentives to prevent short-term thinking.

• Measure performance across several areas, not just one easily-gameable metric

Accountability and Transparency

These strategies revolve around enhanced transparency and clearer communication between agents and principals, with effective auditing and logging of decisions.

1. Intention Sharing Protocols

You should begin critical task delegation with explicit discussion of underlying purposes. Document and reference these throughout the process.

Create formal “Intention Alignment Sessions” at key project stages where agents reread original charters, review success criteria, discuss how current activities connect to original intentions, and document goal adaptations with clear rationales. Other AI models or human overseers can support this process, similar to Claude’s self-critique and Critic models used in systems like Claude Plays Pokemon.

2. Active Listening Frameworks

You can implement structured processes for agents to reflect back their understanding. Ask agents to articulate how their approaches serve the principal’s goals. Create “challenge sessions” where agents can question assumptions constructively.

3. Radical Transparency Systems

This is an attempt to make work progress and decision-making visible to all stakeholders. Document rationales for key decisions. Obviously chain-of-thought is an attempt to do this, but it has problems in that many of the chain-of-thought outputs are unfaithful. Research into how to make chain-of-thought more reliable is ongoing, although some techniques like outcome-based RL seem to enhance faithfulness by small but significant amounts.

Create open access to information to enable informal monitoring. Make the reasoning behind decisions visible to all parties. Developing enhanced interpretability techniques will help make neural network decisions visible to stakeholders.

4. Narrative Alignment

You can develop shared stories about mission and purpose. Connect specific tasks to broader organizational narratives, and create opportunities to reinforce these narratives throughout the work.There’s been some research into how AI systems respond to narrative priming, but no specific research into using organizational storytelling techniques like shared mission narratives and connecting tasks to broader organizational stories.

5. Regular Reporting and Visibility

Structured reporting should become more than a bureaucratic exercise. Genuinely valuable reporting standards consist of check-ins that create transparency, allowing all stakeholders to understand progress, challenges, and interconnections. By standardizing reporting formats, organizations create a common language of accountability.

6. Collaborative Planning

Joint planning sessions can break down traditional hierarchical barriers. By involving agents directly in strategy development, organizations ensure that those executing the work deeply understand the context and reasoning behind decisions. This approach transforms team members from passive implementers to active strategic partners.

7. Technological Enablers

Project management tools can serve as strategic transparency instruments—shared digital spaces that visualize progress, create documentation trails, and enable real-time collaboration across teams and hierarchical levels, supporting networked accountability frameworks.

Organizations can move beyond hierarchical structures toward dynamic accountability models featuring team-based responsibility distribution, nested responsibility circles with clear handoffs, redundant oversight for critical areas, and network governance emphasizing relationships over rigid reporting.

8. Mutual Monitoring Systems

Design systems where agents become mutual monitors and accountability partners—AIs watching AIs—creating collaborative ecosystems where transparency emerges organically through mutual engagement. AI Safety via Debate is approach where agents are trained to debate topics with one another, using a human to judge who wins, with the goal of training AI systems to perform far more cognitively advanced tasks than humans are capable of while remaining aligned with human preferences. Recent work includes developing alignment auditing agents that autonomously uncover hidden goals, build behavioral evaluations, and surface concerning LLM behaviors.

Technology-Driven Solutions

Digital Twins of Intent

Create digital representations of the principal’s intent that agents can refer to, this includes things like inverse reinforcement learning. Use AI systems to suggest alignment checks based on documented intent. Develop recommendation systems that suggest course corrections.

Blockchain-Based Smart Contracts

Implement programmable agreements that automatically enforce certain constraints. Create immutable records of commitments and agreements. You might be able to use Zero-Knowledge proofs to verify agreement on a set of tasks between negotiating parties.

Design milestone-based release mechanisms for resources. The closer an AI agent gets to completing all the required sub-steps of a task, the more access to the needed resources it has.

Real-Time Alignment Dashboards

This involves developing systems that track key indicators of alignment in real-time. Create visualizations that highlight potential divergence from goals. Implement early warning systems for goal drift. Create real-time visibility into key performance indicators.

There are some existing AI testing and LLM evaluation platforms with live dashboards that track performance across every update to catch drift, regressions, and emerging risks early. The most developed area is model drift detection, which shares some conceptual overlap with alignment monitoring, but this could be expanded to things like:

1. Real-time alignment metric visualization - Most research focuses on post-hoc analysis rather than live tracking of alignment indicators.

2. Goal divergence early warning systems - While model drift detection exists, goal drift detection specific to alignment is underexplored.

3. Alignment-specific KPIs and dashboards - Current monitoring focuses on performance metrics rather than value alignment indicators.

4. Predictive alignment monitoring - Early warning systems that predict alignment failures before they occur.

Collaborative Documentation Platforms

Use tools that make the evolution of plans and decisions transparent. Enable shared annotation of changes and decision points, while creating audit trails of how and why approaches evolved.

Workflow systems: Implement processes that guide work through approved pathways

Communication platforms: Utilize tools that enhance transparency and documentation

Nested Accountability Systems

Create intermediate checkpoints where the agent must demonstrate alignment with original goals. Iterated amplification is a version of this, where smaller sub-tasks of a task are manually checked for alignment by human overseers. Then larger tasks are broken into subtasks and checked for alignment. This is repeated iteratively. You could implement a “step-back” process where the agent must periodically re-align itself with the principal’s objectives, by being able to faithfully decompose a task into subtasks and demonstrate that each of these subtasks are aligned.

Develop tiered approval systems where critical decision points require consultation. Define the dependencies between components to highlight critical path items

Delegation Contracts

Create explicit “contracts” that define success criteria before work begins. Include both outcome metrics and process requirements. Clarify boundaries of discretion and areas requiring consultation. Specify reporting requirements and evaluation criteria. There has been some prior work on agent-based service delegation, and connecting individual contracts supporting external enforcement structures. Recent research on authenticated delegation addresses the technical infrastructure for delegation contracts.

It could be worth exploring:

1. Integrated frameworks that combine explicit success criteria, process requirements, discretion boundaries, and evaluation criteria in a unified contract system

2. Real-time contract monitoring that tracks adherence to both outcome and process requirements

3. Dynamic contract adjustment based on performance and changing requirements

4. Standardized contract templates for common AI delegation scenarios

Decision Rights Matrices

Develop clear decision rights frameworks (RACI matrices - Responsible, Accountable, Consulted, Informed). Specify which decisions the agent can make independently vs. which require approval. Define escalation paths for ambiguous situations.

Forrester has created a RACI matrix tool specifically for AI governance, helping organizations understand “which leaders should be responsible, accountable, consulted, and informed” for activities in the NIST AI Risk Management Framework’s four functions: govern, map, measure, and manage.

Integrated approaches combining delegation contracts with decision rights matrices appear underexplored:

1. Formal RACI-Contract Integration: No research found that systematically combines explicit delegation contracts with formal RACI matrices for AI systems

2. Alignment-Specific Decision Rights: Most RACI research focuses on project management rather than value alignment and safety considerations

3. Dynamic Authority Allocation: Limited work on how decision rights should evolve based on AI capability and performance

4. Cross-Functional Alignment Governance: Gap in research on how different stakeholders (technical, ethical, legal, business) should coordinate decision rights for alignment

Cultural and Psychological Approaches

Ethical Simulation Training

In this scenario you would train agents to mentally simulate the principal’s perspective. This is done with inverse reinforcement learning, and it seems to help AI agents understand ethics. Waymo uses similar simulations for the decision processes in their autonomous vehicles.

Create “ethical pre-mortems” where potential misalignments are anticipated. Develop case studies of successful and unsuccessful alignment.

Values-Based Selection

This involves selecting agents based on demonstrated alignment with organizational values. Create interview processes that reveal ethical reasoning. You can use structured ethical interview processes to assess AI systems’ moral reasoning capabilities. For example, some researchers conducted ethics-based audits where AI models were instructed to extract relevant ethical factors, assign weights to them, and use these as a form of moral calculus to determine appropriate actions.

Design trial periods that test alignment under realistic conditions.

Community of Practice

Create communities where agents can share best practices for maintaining alignment. You can perhaps create an index of known safety constraints and allow AI agents to access it and learn from it, and let them update the list of known constraints, creating a kind of race-to-the-top dynamic. STELA is a method of eliciting feedback from both experts and community input for the purposes of AI alignment.

Develop peer coaching systems for navigating alignment challenges. Establish learning systems to capture and distribute alignment experiences. Encourage agents to provide feedback to principals on how to improve delegation

Identity Integration

Help agents integrate organizational purposes into their personal identity. This might look like having the agent create policy commitments align with group values and goals. Create opportunities for agents to contribute to organizational values

Design role-crafting processes that align personal meaning with organizational purpose. Invest in thorough orientation to organizational culture and value.

Shared values development:

Explicitly discuss and document shared values and principles. Encourage explicit discussion of AI values, as you may find that LLMs are capable of learning specific patterns of values. However, this requires more research as recent findings suggest that such values are unstable and context-dependent.

Model cards may play a role in stating the values and principles of certain models, enabling users to select the models with values that augment theirs. Currently standards for model cards are highly variable.

Create opportunities for principals and agents to build relationships. Research into allowing AIs to build trust with each other and humans.

Establish clear ethical guidelines for decision-making.This is of course the rationale behind Constitutional AI, but be wary of specification gaming.

Speaking of specification gaming…

Risk Management Approaches

Ethical Red Teams

Designate people to play the role of “ethical hackers” who try to identify ways tasks could be misinterpreted, functioning as an ethics red-team. Their goal would be to identify situations that lead to specification gaming and reward hacking.

Create structured challenges to test the robustness of task specifications.Implement preventive interventions based on identified vulnerabilities.

Scenario Planning for Misalignment

Proactively identify conditions that might create incentives for misalignment. Goal misgeneralization seems to be the most common failure mode such as ambiguous task specification, distribution shifts, poorly designed reward functions. Opaque reasoning abilities and neuralese make this difficult to do, but high impact if the problem can be cracked.

Develop contingency plans for addressing potential misalignment, research into interpretability techniques like sparse auto-encoders, will be important, goal misgeneralization detection. Create early detection systems for misalignment risk factors.

Bounded Autonomy

Define clear guardrails within which agents have freedom to operate. Something like bounded autonomy represents an emerging framework in AI safety that allows AI agents to operate independently within well-defined constraints while preserving human oversight.
Operational boundaries, permissions-based boundaries, anomaly detection triggers, resource consumption boundaries, ethical and values boundaries. Role-based access control (RBAC): Restricts AI access to sensitive systems based on user roles .

Ethical guardrails: Prevent biased or harmful outputs by aligning AI with human values (e.g., filtering discriminatory language)

Technical guardrails: Block prompt injections, hallucinations, or unauthorized data access

Create escalation triggers when approaching boundaries, using concepts like graduated control systems, real-time monitoring and alerting, and sandboxing with controlled escalation. Design systems that allow quick response to boundary violations based on categorized risk.

Risk Categorization
Incidents are classified by severity (low/medium/high), with predefined response timelines.

For example:

Medium risk: Data bias flagged for review within 48 hours

High risk: Toxic content or PII leaks blocked immediately

Diversity of Perspective

Include diverse stakeholders in oversight processes. Create checks and balances through multiple perspectives. Implement rotation of oversight responsibilities.

Governance and Oversight Approaches

Clear contracts and expectations: Document explicit responsibilities, deliverables, and timelines. evolving from static legal agreements to dynamic technical specifications that must handle AI’s probabilistic nature. Companies contracting with AI developers must consider whether developers should assist with compliance obligations, while contractualist AI alignment frameworks propose using temporal logic statements that compile to non-Markovian reward machines for more expressive behavioral constraints

Staged delegation: Start with close oversight, increasing autonomy as trust develops. Research shows people prefer delegating to AI over humans, especially for decisions involving losses, due to reduced social risk premiums. However, Information Systems-invoked delegation (when systems request autonomy) increases users’ perceived self-threat compared to user-invoked delegation.

Decision thresholds: Establish clear boundaries for when agents need to consult principals. Risk thresholds establish concrete decision points that trigger responses or escalation, with training compute thresholds attracting significant regulatory attention. Anthropic’s ASL system exemplifies this with graduated safety measures that become more stringent as capabilities increase, including specific thresholds for CBRN capabilities and autonomous AI R&D.

Third-party verification: Use independent parties to audit or verify critical outcomes Third-Party Verification faces significant structural challenges. Independent oversight requires addressing barriers like lack of protection against retaliation, limited standardization, and risks of “audit-washing” where audits appear independent but aren’t truly arms-length. Proposals include national incident reporting systems, independent oversight boards, and mandated data access for certified auditors.

Milestone reviews: Schedule regular progress assessments and realignment opportunities. Integration into corporate governance requires boards to monitor AI-specific goals with consistent evaluation methods, while regulatory frameworks implement phased compliance requirements.

These complementary strategies address structural, incentive, communication, technological, cultural, and risk dimensions of principal-agent relationships across organizational and technological contexts.

The second post in this series will involve using the System-Theoretic Process Analysis to analyze principal-agent risks in AI alignment.