AI You Can Trust, Decisions You Can Audit
AI should do more than generate answers – it should verify, refine, and justify them. That’s exactly what Agentic Guardrails enable. With built-in verification techniques, your AI becomes not just powerful, but accountable and self-correcting.
What Are Agentic Guardrails?
Agentic Guardrails provide a framework that uses large language models (LLMs) as reasoning agents, performing iterative validation and refinement on AI-generated content. Instead of passively accepting model outputs, Agentic Guardrails ensure that AI critically evaluates its own responses, improving both accuracy and reliability.
The Agentic Guardrails Breakdown
Each Agentic Guardrail performs a different type of verification or refinement, ensuring outputs meet specific quality benchmarks. For some cases, multiple guardrails may be used to get the desired outputs.
| Guardrail | How It Works | What It Solves | Outcome |
|---|---|---|---|
| LLM as a Judge (Baseline) | Directly asks the LLM to evaluate a claim without additional verification steps. | Baseline comparison for other methods, providing a simple but unverified answer. | A quick but unverified answer from the LLM. |
| Consistency Check | Generates multiple variations of a question and checks if the LLM provides consistent responses. | Detects hallucinations by identifying inconsistent outputs. | Confidence level of answers increased based on output stability. |
| Consensus | Queries multiple LLM instances or variations to see if they agree on an answer. | Mitigates bias and random errors by aggregating multiple responses. | Final answer determined by majority agreement. |
| Critique + Revise | LLM critiques its own answer, revises it, and iterates until improvements stabilize. Generates alternative (counterfactual) scenarios to challenge the AI’s conclusions. | Improves accuracy by refining responses through structured self-review and ensures reasoning holds up under different conditions. | A more robust and logically sound AI-generated response that withstands hypothetical challenges. |
Customize AI Verification to Fit Your Needs
Agentic Guardrails give you the flexibility to choose the verification method that fits your workflow best — without having to go it alone. We guide, configure, and even do the heavy lifting, so your team can stay focused on what matters.
Jaxon's DSAIL Finance Benchmark
To evaluate performance, we used GPT-4o for both the baseline and two DSAIL ‘guardrails’. The case study processed verified QA pairs from FinanceBench, where both baseline and Jaxon models were applied to each pair. We then compared the quality of responses and hallucination rates.
The Results
We compared 3 types of runs and used the averaged F1 scores to determine accuracy, a metric that combines ‘precision & recall’.
Jaxon's Agentic Guardrails boosted F1 performance by 8.25%!
Not the Right Guardrail for You?
If you need a strict, rule-based verification system that ensures AI outputs comply with policies and regulations, check out Policy Rules – our structured compliance framework designed for regulatory adherence and business governance.
The Benefits of Built-In AI Validation
Agentic Guardrails aren’t just about accuracy — they’re also designed to be:
Adaptive
Dynamically refine AI outputs through iterative validation.
Reliable
Reduce hallucinations and ensure consistency across responses.
Configurable
Customize verification workflows to fit your AI’s unique needs.
AI verification shouldn’t be an afterthought. Agentic Guardrails ensure every AI-generated response is fact-checked and trusted — ensuring reliable AI at scale.
