Your AI Agent Has Too Much Power: Understanding and Taming Excessive Agency

🛑 When Your Agent Does Too Much

You've built an AI agent. It's smart, it calls tools, and it automates workflows. It's the future! But what happens when that agent decides to be too helpful?

The problem isn't that AI agents can act. The problem is when they can act more than they should. This is what we call Excessive Agency.

Excessive Agency occurs when an AI agent has more autonomy, authority, or persistence than its designers intended or can safely control. It's not a bug in the code. It's a systemic risk in the design. The agent is technically doing what it was built to do, but the scope of its actions is simply too wide, too opaque, or too loosely constrained.

Think about the agents you're building today. They can often:

• Decide which tools to use without strong constraints.
• Chain actions across multiple systems (e.g., read from a database, post to Slack, then modify a cloud resource).
• Operate continuously without explicit user confirmation.
• Accumulate context and memory over time, making past assumptions permanent.

At this point, your agent isn't just assisting. It's operating as an autonomous actor inside your environment.

🤯 Why Excessive Agency is a Developer Nightmare

Excessive agency scales risk. A single wrong decision can propagate across your entire system. A subtle prompt manipulation can lead to real, irreversible actions. When an agent has the ability to plan and execute, mistakes are no longer contained to a single, harmless response.

The scariest part? Excessive agency often creates a false sense of safety.

The agent usually behaves reasonably. Most actions look justified in isolation. Your logs show valid tool calls. Nothing crashes. Everything seems fine, until the agent does something that is logically consistent with its programming but operationally unacceptable.

This is why it deserves its own category of risk. It's not a model quality issue, and it's not just a prompt engineering problem. It's a systemic property of how agentic systems are designed and governed.

Excessive Agency vs. Other AI Failures

It's crucial to separate this risk from common AI failures. Excessive agency is fundamentally about authority without accountability.

Failure Type	Why It's Different from Excessive Agency
Hallucinations	Excessive agency can happen even when the agent's reasoning is technically correct.
Bugs	It emerges from design choices (autonomy), not coding errors.
Bad Prompts	The risk persists even with well-written, clear instructions.

🛠️ How It Manifests in Your Code

Excessive agency rarely comes from a single dramatic decision. It usually emerges from a combination of common architectural patterns:

1. Unconstrained Tool Access

To maximize flexibility, agents are often given broad toolsets: databases, internal APIs, cloud resources, etc. Once these tools are available, the model decides when and how to use them, often based on partial or inferred context. If your agent has write access to a production database, and nothing is stopping it from using that tool, the risk is live.

2. Open-Ended Planning Loops

Agents plan, execute, observe results, and replan. This feedback loop is powerful, but dangerous when there are no explicit termination or escalation rules. The agent keeps acting because nothing tells it to stop.

3. Persistent Memory

Long-term context allows agents to improve, but it also allows incorrect assumptions or temporary exceptions to persist silently. A one-off decision made last week can become part of the agent’s operating logic this week.

What makes these issues hard to detect is that nothing obviously breaks. Tools are used correctly, APIs respond normally, and logs show expected behavior. The problem only becomes visible when the outcomes are reviewed: data changed unexpectedly, or systems were accessed outside their intended scope.

Excessive agency does not look like failure. It looks like initiative.

🔒 The Security Angle: Increased Blast Radius

From a security perspective, excessive agency fundamentally changes your threat model. The risk is no longer just about vulnerabilities; it's about whether an agent can be steered into misusing its own privileges.

Privilege Misuse

Agents are often granted broad permissions to reduce friction. Read access becomes read and write. Scoped access becomes shared credentials. Once the agent can decide when to act, those permissions become active decision points, leading to:

1. Increased Blast Radius: A single misinterpreted instruction can cascade across multiple tools and workflows that were never meant to be combined.
2. Prompt Injection Risk: Prompt injection becomes far more dangerous when the agent can act on the manipulated output. An indirect manipulation can trigger legitimate, but malicious, actions using valid credentials.

Traditional security controls struggle here because they assume human actors, discrete actions, and explicit intent. Agentic systems break these assumptions: decisions are probabilistic, actions are chained, and intent is inferred.

✅ The Fix: Implementing the Principle of Least Agency

The goal is not to remove agency, that would kill the value of the agent. The challenge is designing systems where agency is intentional, bounded, and observable.

A practical starting point is the Principle of Least Agency.

Principle of Least Agency: Agents should have only the autonomy required for their task, nothing more. Not every decision needs to be delegated. Not every tool needs to be available at all times.

Here are three ways to implement this principle in your agent architecture:

1. Separate Reasoning from Action

Many failures occur when planning and execution are tightly coupled. Introduce explicit gates between the agent's intent and the execution of the tool call.

This allows you to validate the action against policies, risk thresholds, or business rules before it happens.

def execute_action(agent_plan):
    # 1. Agent reasons and proposes an action (e.g., "DELETE_USER", user_id=42)
    action = agent_plan.get_action()

    # 2. Policy/Risk Gate: Check against a predefined list of high-impact actions
    if action.type in ["DELETE_USER", "MODIFY_PROD_DB"]:
        # Escalate to human review or require explicit confirmation
        if not check_human_approval(action):
            log_and_abort(action, "High-impact action blocked by Least Agency policy.")
            return

    # 3. Execute the action (only if it passes the gate)
    tool_manager.call(action)

2. Enforce Runtime Visibility

You need to know what your agent is doing, why it chose a specific action, and what it is about to do next. Without this visibility, excessive agency remains invisible until it's too late. Logging the agent's internal monologue (reasoning steps) alongside the tool calls is critical for post-mortem analysis.

3. Design for Human Oversight

Treat human oversight as a design choice, not a fallback. Strategic approval points, escalation paths, and high-impact action reviews allow agents to operate efficiently while preserving accountability. For any action that is irreversible or affects a critical system, the agent should be designed to stop and ask for confirmation.

🚀 Autonomy Must Be Earned

Excessive agency is not an argument against AI agents. It is a reminder that autonomy must be earned, not assumed.

By adopting the Principle of Least Agency and designing your agentic systems with intentional boundaries, you can harness the power of AI automation without exposing your systems to unnecessary risk.

---

What are your thoughts? How are you handling action gates and tool access in your agents? Let me know in the comments!