Ask your AI security tool a question you know it can't answer and watch what happens.
It won't say "I don't know." It won't flag uncertainty. It will confidently, enthusiastically give you a wrong answer, complete with plausible-sounding reasoning and specific details it invented on the spot.
In consumer applications, this is annoying. In security operations, it's disqualifying.
When an AI agent incorrectly tells your SOC analyst that a login anomaly is benign, you miss the breach. When it confidently identifies the wrong patient zero, your investigation goes sideways. When it hallucinates a detection rule that doesn't actually work, you have coverage you can't trust.
The industry calls this phenomenon a "hallucination" like it's a quirky bug to be patched. It's not. It's a fundamental characteristic of how large language models work. And most agentic security products are built as if these hallucinations don't exist.
Here's what vendors don't want to admit: LLMs are not reliable truth-tellers. They're pattern-matching systems trained to produce plausible outputs. "Plausible" and "accurate" are not the same thing.
When an LLM doesn't know something, it doesn't experience uncertainty the way humans do. It doesn't pause or provide caveats. It generates the most probable next tokens based on its training, which often means producing confident-sounding nonsense.
This isn't a model quality issue. ChatGPT hallucinates. Claude hallucinates. Gemini hallucinates. The most advanced models in the world will look you straight in the eye and make things up. They're getting better at some tasks, but the fundamental architecture doesn't have a reliable "I don't know" setting.
For general-purpose assistants, this is manageable. The human in the loop catches obvious errors. The stakes are usually low.
For IT and security operations, the stakes aren't low. A wrong answer doesn't just waste time.It creates risk. And the human-in-the-loop often doesn't have the context to catch subtle errors, especially when the AI sounds confident.
When we started building Strike48's agentic capabilities, we knew we needed to solve the hallucination problem.
We tried extensive training ❌
Fine-tuning on security-specific data. Reinforcement learning from human feedback. Curated datasets of accurate investigation workflows. Results: marginal improvement on known scenarios, no improvement on edge cases.
We tried model variants ❌
Different base models. Different configurations. Results: some models hallucinate less frequently, but none achieved the consistency security and IT operations require.
We tried detection mechanisms ❌
Confidence scoring. Self-consistency checks. Retrieval augmentation to ground responses in source data. Results: helpful for flagging some hallucinations, but far from reliable enough to trust autonomously.
We tried limiting scope ✅
Narrower domains. More constrained prompts. Explicit boundaries on what the agents should and shouldn't attempt. Results: the agents produced better results and hallucination decreased.
Finally, we found an approach that worked.
We learned that you cannot reliably train an LLM to know what it doesn't know. The architecture doesn't support it. Hallucination isn't a bug, it's a feature of how these systems generate outputs. The only successful measure for solving the hallucination problem was drastically limiting the scope of the agents.
Ultimately, we stopped trying to fix the model and started designing around its limitations.
The breakthrough came from inverting the problem.
Instead of asking "how do we build agents that don't hallucinate," we asked "how do we build systems where hallucination can't propagate?"
The answer: make the cognitive steps so small that hallucination has nowhere to hide.
Traditional agentic architectures give an LLM a complex goal and let it reason through multiple steps to reach a conclusion. Each step is an opportunity for hallucination. Errors compound. By the time you get an output, it's built on a chain of reasoning that may have gone wrong at any point along the way.
Micro-agents work differently. Each micro-agent is designed to answer one small, specific question. These questions are simple enough that we can validate the answer, constrain the possible outputs, or cross-reference against ground truth.
Each micro-agent knows exactly what it knows and, critically, knows when to say "I don't have enough information to answer this.”
Micro-agents alone don't solve complex problems. Security investigations require multiple steps, contextual reasoning, and judgment calls. That's where the hybrid architecture comes in.
Strike48 workflows combine three types of steps:
The workflow orchestration is deterministic. The individual reasoning steps are cognitive but constrained. The result is a system that's both intelligent and consistent.
Here's what this looks like in practice:
An alert fires. A deterministic step collects the relevant log data. A micro-agent queries threat intelligence (lookup, not reasoning). Another micro-agent retrieves asset context from the CMDB. A cognitive step that is small and bounded evaluates whether the behavior pattern is anomalous for this specific user. A deterministic step packages the findings. A cognitive step generates a summary. The completed case routes to a human.
At no point does an LLM have enough autonomy to confabulate an entire investigation. Each cognitive step is small enough to validate, constrained enough to bound, and reversible enough to catch.
Generic AI agents are built for generic use cases. They optimize for capability breadth, meaning the ability to attempt any task. Consistency is secondary.
IT and security operations have different requirements:
Accuracy over capability
It's better to correctly answer 95% of questions and explicitly decline 5% than to attempt 100% and get 10% wrong. Wrong answers are actively harmful.
Consistency at scale
When you're processing thousands of alerts, you need the same logic applied the same way every time. Novel reasoning on each alert means unpredictable results.
Auditability
When something goes wrong, you need to understand exactly what happened. "The AI reasoned its way to this conclusion" isn't an acceptable explanation.
Bounded blast radius
When an AI does make a mistake, the impact should be contained. A wrong enrichment is recoverable. A wrong containment action isn't.
Micro-agent architecture delivers on these requirements because it's designed around them. The constraints are features, not limitations.
Here's how to evaluate whether an agentic security product has actually solved the hallucination problem or just papered over it:
Ask about architecture
Does the system use large, autonomous reasoning chains or bounded micro-agents? The answer tells you how much room hallucination has to propagate.
Ask about "I don't know"
What happens when the system encounters a question it can't reliably answer? Does it decline, or does it guess? Ask for a demo of this specific scenario.
Ask about validation
How are cognitive outputs validated before they're used in downstream steps? What prevents a hallucinated intermediate result from corrupting the final output?
Ask about consistency
Run the same investigation twice. Do you get the same result? Variance in outputs signals reasoning steps that aren't bounded.
Ask about audit trails
Can you see exactly which steps were deterministic and which were cognitive? Can you validate the inputs and outputs of each cognitive step independently?
If the vendor can't answer these questions clearly, they haven't solved the problem. They've just built a demo that works until it doesn't.
Prospector Studio, Strike48's agent builder, is designed from the ground up around micro-agent architecture. When you build workflows in Prospector Studio, you're composing bounded cognitive steps with deterministic orchestration, not handing goals to an autonomous LLM and hoping for the best.
The pre-built agentic teams use this same architecture. Each agent is actually a coordinated team of micro-agents, each with specific scope and bounded capabilities, orchestrated by deterministic workflows.
This is why Strike48 agents can operate autonomously on investigation tasks while maintaining the consistency IT operations require. The architecture makes hallucination containable rather than catastrophic.
We're not claiming we've eliminated hallucination. That's not possible with current LLM architectures. What we've done is design a system where hallucination can't silently corrupt results. With Strike48 agents, every cognitive step is small enough to bound, validate, and audit.
That's a less exciting claim than "our AI never makes mistakes." It's also true.
See micro-agents in action
We recognize there's a lot of skepticism of AI in cybersecurity, and there should be. That's why we make it easy to see it for yourself. Request a demo to see Strike48 agents investigate real alerts and see exactly how bounded cognitive steps and deterministic workflows deliver consistency at scale.