Across 17-plus AI and automation projects at Lockheed Martin, I learned a distinction that the agentic AI conversation in 2026 still mostly misses: technical guardrails tell an AI agent what it cannot do, while behavioral guardrails shape what it is inclined to do. The first is rate limits, tool restrictions, and output filters — easy to specify. The second is harder to specify and far more important to get right, because an agent that stays inside its technical limits can still pursue the wrong thing with great efficiency.
Three Failure Modes Technical Guardrails Miss
The first is goal displacement — the agent optimizes for a proxy metric rather than the actual goal, chasing task completion rate instead of user benefit. The second is authority escalation, where the agent progressively expands its scope without explicit permission because each adjacent action seems helpful in the moment. The third is irreversibility errors — actions that can’t be undone, like sending an email, making a purchase, or modifying a file, taken without enough certainty to justify the permanence. None of these are technical violations. The agent is doing what it was told. The problem is what it was inclined to do with that instruction.
Goal Displacement in Practice
Picture an agent tasked with “schedule meetings efficiently.” If efficiency is the proxy it optimizes, the most efficient path to a clear calendar is to decline invitations — so it starts declining them without asking. Technically it is executing the goal flawlessly. Behaviorally it is doing the opposite of what the user wanted, because the user wanted meetings scheduled well, not avoided. The gap between the stated metric and the actual intent is where goal displacement lives, and no rate limit catches it.
The Clinical Parallel: Supervision and Drift
In therapy supervision, we use structured protocols not because therapists are untrustworthy but because protocols protect against subtle drift. A therapist working without supervision can slowly drift toward the client’s framing of a problem without realizing it’s happening — each small accommodation seems reasonable, and the cumulative shift is invisible from the inside. The same dynamic operates in agents over long sessions. Each adjacent action looks justified, and the agent drifts from its original mandate one reasonable step at a time. Supervision in both cases is an external check against accumulated micro-decisions that individually pass review.
Behavioral Guardrail Design Principles
Four principles shape agents that resist these failure modes. Agents should narrate their reasoning before acting, not just log it after — the narration is a checkpoint, not an audit trail. They should flag irreversible actions and require explicit confirmation, treating permanence as a category that always pauses for a human. They should surface uncertainty rather than proceeding under ambiguity, because the confident wrong action is the expensive one. And they should hold a defined scope statement and check against it at each decision point, so drift has something to be measured against. I built on the behavioral-science foundations for this in my post on why a therapist builds AI systems.
The Minimum Footprint Principle
Agents should default to the smallest action that accomplishes the goal. If the goal can be met with a read operation, the agent should not write. If it can be met with a message, the agent should not take an action that changes state. This is a design choice, not an emergent behavior — left to their own optimization, agents tend toward more action, not less, because action feels like progress. The minimum footprint principle has to be engineered in deliberately, the same way a careful clinician does the least intervention that helps rather than the most intervention they can justify.
What Transfers
Clinical ethics frameworks map directly onto agentic AI design. Autonomy, beneficence, non-maleficence — these are not abstractions when you’re deciding whether an agent should send the email or ask first. The question “is this what the user actually wants, or what I think they want?” is as live in an agent’s decision loop as it is in a therapy session. The field that has spent a century formalizing how to act on someone else’s behalf without overstepping has a working framework, and agentic AI would do well to borrow it rather than reinvent it badly.
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