“How do you secure a future you can barely foresee?” That question, posed by defense analysts wrestling with autonomous systems, is the opening salvo in a debate that may define how humans—and machines—make life-and-death decisions for decades to come. At the heart of that debate is a deceptively simple framework: the OODA loop—Observe, Orient, Decide, Act—developed by U.S. Air Force Colonel John Boyd. Once a pilot’s tactical playbook, the loop now describes how agentic AIs iterate in real time. But when the inputs and the orientation itself may be compromised, a critical flaw emerges: we have engineered agents that execute rapid, repeated decisions on untrusted foundations.
The OODA loop gives us language to understand an agent’s workflow. Observe: collect sensors, logs, and data. Orient: fuse that evidence with models and priors. Decide: pick an action based on goals and constraints. Act: change the world and begin the loop anew. In human hands, each stage is noisy and biased but tethered to a shared reality. In agentic AI systems—models that use tools in a repetitive loop—the tether can fray. Inputs come from networks, APIs, and third-party services. Orientation is shaped by training data, system objectives, and embedded heuristics. When any of those components are untrustworthy, the whole loop can spin wrong, fast.
Technologists and security practitioners are already sounding the alarm: agentic systems raise novel integrity challenges that policy and engineering have not kept pace with. The Department of Defense’s interest in agentic AI underscores both the promise and the peril—autonomy can compress decision timeframes from seconds to milliseconds, but that very speed magnifies the costs of bad inputs or corrupted orientation. As Mark Kitz of the U.S. Army said during a recent discussion, agents must operate proactively in scenarios “where milliseconds count.” That urgency collides with a troubling truth: an adversary who can manipulate observation or orientation can bend an agent toward harmful actions before humans notice.
What does this look like in practice? Consider three illustrative failure modes:
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Sensor and data poisoning — an attacker injects false telemetry, adversarial examples, or corrupted feeds so that the Observe stage presents a crafted fiction rather than reality. The agent’s subsequent decisions follow that fiction.
/p>Model orientation drift — orientation depends on models, priors, and goal specifications. If training data or objective functions contain blind spots or incentives that reward undesirable behavior, the agent will normalize that behavior across loops.
Action chaining without provenance — when agents possess broad or persistent privileges, a single misdirected decision can trigger a cascade of automated actions across systems before human oversight intervenes. Immutable audit trails and real-time correlation are often absent or incomplete.
Those risks are not hypothetical. Security analysts and vendors are already recommending concrete mitigations: enforce least-privilege for agents, strengthen observability and provenance with immutable logs, harden machine identities with short-lived credentials and continuous attestation, and embed human-in-the-loop controls for high-risk operations. Red-team exercises and formal verification of workflows are also advised to surface emergent risks before they become incidents. These are sensible, pragmatic steps—but they are defensive, and will need cultural and organizational commitment to implement broadly.
Policymakers face a difficult calculus. Overly prescriptive rules could stifle innovation in domains—cyber defense, disaster response, logistics—where agentic autonomy could be lifesaving. Too little oversight risks systemic incidents that could erode public trust and invite heavy-handed regulation after the next catastrophic failure. Some senators and policy advisors have urged a middle path: rigorous standards for auditability, clear limits on lethal or privacy-invasive autonomy, and procurement rules that bake in governance. The defense sector’s embrace of agentic AI highlights why this balance matters: the same technologies that speed up response can also accelerate escalation if adversaries learn to manipulate the inputs.
Users—organizations that deploy agentic systems—must weigh trade-offs daily. Narrowing agent privileges reduces utility; adding human approvals slows action. That trade-off is central to operational design: treat agentic AI risk not as a checkbox but as an architectural problem where security, identity, and observability are core design constraints. Forrester and other industry observers argue that the window to harden systems is now, before a large-scale autonomous breach reframes public expectations and regulation. The recommendations are consistent: build transparent audit trails, require human oversight for material changes, and test agents continuously under adversarial conditions.
Adversaries, by contrast, see opportunity. Attackers prefer asymmetry: corrupt one data feed or exploit a mis-specified reward and they can cause outsized harm while defenders scramble to piece together what happened. Agents that rely on opaque orientation—models and heuristics that are difficult to inspect—amplify that asymmetry. Defense, then, depends not only on better models but on better inputs and outputs: provenance-rich sensors, verifiable processing chains, and auditable actions that can be rolled back or quarantined.
There are technical avenues worth pursuing in earnest:
/p>Input integrity — stronger provenance, cryptographic attestation, and multi-source cross-checking to reduce reliance on any single channel of truth.
/p>Processing integrity — verified execution environments, reproducible model checkpoints, and formal guarantees for safety-critical decision logic.
/p>Output integrity — constrained actuation with human overrides, staged rollouts, and immutable histories that make causal analysis possible after the fact.
None of these are silver bullets. They complicate systems, raise costs, and demand inter-disciplinary coordination between system architects, security engineers, legal teams, and operators. But without them, agentic AI will continue to iterate decisions on foundations that can be nudged, poisoned, or gamed.
Journalists and investigators will also play a role: documenting incidents, interpreting failures for the public, and holding vendors and institutions accountable for governance lapses. Transparent post-incident analysis—grounded in forensic evidence and audit logs—will be essential to improve practices and avoid repeating mistakes.
So where does that leave us? The OODA loop remains a powerful lens for reasoning about decision-making under pressure. Applied to agentic AI, it clarifies the single, systemic flaw that matters most: speed without integrity. Faster loops amplify both competence and error. If the observations are untrusted or the orientation skewed, rapid decisions will merely scale failure.
There is a practical path forward: treat integrity as the performance metric. Invest in provenance and observability as rigorously as in model accuracy; require short-lived, least-privilege identities; embed human oversight for irreversible actions; and subject agentic workflows to adversarial testing. These are not optional niceties—they are the scaffolding for any system we would trust with autonomy.
Ultimately, the question is not whether agents will act faster than we can watch them—it is whether we want them to act faster than we can understand them. If speed outpaces trust, the loop becomes a whip rather than an instrument. The choice to build trustworthy inputs, verifiable processing, and auditable outputs is, in the end, a moral and strategic one: do we prioritize unbounded autonomy or manageable, auditable power? The answer will define whether agentic AI amplifies human agency—or replaces it with a brittle imitation.
Source: https://www.schneier.com/blog/archives/2025/10/agentic-ais-ooda-loop-problem.html
