In our last blog post, “Anchoring Agentic AI with Enterprise Orchestration—Part 1,” we explored why workload automation (WLA) platforms aren’t just compatible with agentic AI—they are essential for its secure and scalable adoption. We also saw how the Model Context Protocol (MCP) is fast becoming the standard that allows probabilistic AI agents to interact with deterministic enterprise systems.
These agents unlock immense potential. However, their unpredictable nature introduces profound risks to governance, explainability, and control. In this post, we examine a key question: How can you safely scale autonomous AI agents in an enterprise environment?
To employ agentic AI safely, enterprises need more than just connections; they need an intelligent control plane and a strict framework of reversible autonomy for AI agents.
Here is how organizations can provide the lineage, auditability, and safety mechanisms required for the AI era.
Mitigating the unpredictable risks of agentic AI is the difference between a profitable adoption and a costly disaster. Here are three scenarios in which autonomous agents present real-world risks:
Agent hijacking (prompt injection): Traditional security models assume an authenticated user's intent is valid. Agents disrupt this paradigm. They might process "poisoned" input and execute fraudulent transactions, all while using perfectly valid credentials.
Semantic infinite loops: In deterministic coding, infinite loops are syntax errors. In agentic systems, loops are semantic. Imagine an optimization agent programmed to "reduce cloud spending" and a reliability agent programmed to "ensure 99.99% uptime." The optimizer turns off a server to save money; the reliability agent immediately turns it back on. The result is an undesirable infinite loop.
Drift and hallucination: Unlike traditional code, which only breaks when the environment changes, AI models can "drift" or hallucinate. An agent might decide that "deleting the system logs" is a highly effective way to resolve a "disk space warning"—technically solving the problem, but introducing a massive violation of compliance policies.
We can address these risks—and others we haven't even thought of yet—by using the proven, time-tested functionalities intrinsic to traditional orchestration tools. (Read this post to learn more about how agentic AI orchestration can help scale workflow automation.)
To mitigate these risks, organizations will increasingly require an intelligent control plane. How can an intelligent control plane mitigate agentic AI risk? This control plane is a software layer that sits between the AI agents and your execution systems (such as APIs and databases) to enforce three key capabilities:
Agent identity (non-human identity): Agents must have cryptographically verifiable identities using short-lived, just-in-time (JIT) credentials. An agent should never hold a static API key. It should request a token for a specific action, which expires the moment the job is done.
Policy-as-code guardrails: The control plane enforces rules at runtime. Before an agent's API call is allowed to reach the backend, it must pass through a policy engine. These gatekeepers provide the hard stops needed to prevent damage.
Observability and audit trails: Traditional logs record what happened. Agentic observability must record why. It must capture the agent's "chain of thought," the prompts it received, and the reasoning logic it used, enabling vital post-incident forensics. (See a NIST blog post to learn more about addressing the security risks AI agents can pose.)
Perhaps the most critical innovation for agentic AI risk management is the reversible autonomy framework. How does reversible autonomy protect businesses from AI hallucinations and infinite loops? This framework posits a simple rule: “For an AI agent to be trusted in production, its actions must be reversible.”
While the ultimate goal is pre-emptive gating, reversible autonomy mandates rollback capabilities through these measures:
Compensating transactions: For every "do" action an agent takes (e.g., provision_vm), there must be a mapped "undo" action (deprovision_vm). A traditional orchestration engine enforces this pairing with ease.
State snapshotting: Before an agent executes a write operation on a database, the control plane takes a snapshot. If an AI agent or human supervisor flags the action as erroneous, the system simply "rewinds."
"Human-in-the-loop" circuit breakers: The system continuously monitors the agent's confidence scores. If confidence drops below a defined threshold, or if the financial "blast radius" of an action is too high, the system automatically pauses execution and routes the decision to a human.
By acting as a "safety anchor," automation frameworks provide the exact lineage and auditability that probabilistic LLMs lack. Every agent-initiated action is logged as a discrete job execution within the scheduler. This provides the following capabilities:
Traceability: Every "do" and "undo" pair is tracked with a unique global ID, linking the AI model’s intent directly to system logs.
Atomic pairing: The MCP server doesn't just expose a single "execute" function; it exposes a transaction block. If a job fails, the scheduler automatically triggers the compensating "undo" job.
The circuit breaker: By linking AI confidence scores to SLA thresholds, the scheduler physically halts high-value transactions that lack confidence, placing them in a "hold" state for manual review.
Predictive analytics: Schedulers can forecast if an agent's "deliberation time" is taking too long and threatens to miss an SLA, triggering a proactive human takeover.
Compliance reporting: For regulated industries, the scheduler generates a deterministic audit trail, proving to regulators that human or automated mitigation was actively available for every autonomous action.
The intersection of existing orchestration solutions and agentic AI is not a zero-sum game in which the new replaces the old. It is a convergence.
Analysts predict agentic AI will be the single largest driver of enterprise value over the next five years. However, Gartner also predicts that over 40% of agentic AI projects will be canceled by the end of 2027 due to inadequate risk controls.
By bridging the gap between probabilistic AI intent and deterministic execution, enterprises can achieve consistent, massive gains. Anchoring these AI efforts with robust, dependable orchestration reduces risk by aligning advanced observability with existing business processes. This robust orchestration is a proven, core strength of Automation by Broadcom solutions.
Key risks include agent hijacking through prompt injection, semantic infinite loops, and model hallucinations that violate compliance mandates.
It issues short-lived, just-in-time (JIT) credentials that expire immediately after an agent completes a specific task.
It mandates that for an AI agent to be trusted, every action must have a clear "undo" function.
Orchestration provides deterministic lineage, audit trails, and circuit breakers that stabilize unpredictable, probabilistic AI model outputs.