# InferTheta Blog

Practical guides on AI decision-making under uncertainty—probabilistic AI, agent governance, and high-stakes applications.

Index: https://infertheta.com/blog

## AI Agents

- [How to Build an AI Agent That Knows When It Doesn't Know](https://infertheta.com/blog/ai-agent-knows-when-it-doesnt-know) — Calibrated uncertainty detection prevents agents from acting on guesses. Learn practical methods for epistemic humility in production agent systems.
  - Markdown: [ai-agent-knows-when-it-doesnt-know.md](https://infertheta.com/blog/ai-agent-knows-when-it-doesnt-know.md)
- [How to Build Auditable AI Agent Decisions](https://infertheta.com/blog/auditable-ai-agent-decisions) — Auditable agents produce reproducible decision records that satisfy operators, security teams, and compliance reviewers—not just conversational explanations.
  - Markdown: [auditable-ai-agent-decisions.md](https://infertheta.com/blog/auditable-ai-agent-decisions.md)
- [10 Best AI Decision Engines in 2026](https://infertheta.com/blog/best-ai-decision-engines-2026) — AI decision engines score actions, enforce policies, and log rationale for agents and automation. Here is a fair comparison of leading options for production use in 2026.
  - Markdown: [best-ai-decision-engines-2026.md](https://infertheta.com/blog/best-ai-decision-engines-2026.md)
- [10 Best Platforms for AI Agent Governance in 2026](https://infertheta.com/blog/best-platforms-ai-agent-governance-2026) — Agent governance platforms help teams deploy LLM agents with policies, monitoring, and human oversight. This comparison covers strengths and tradeoffs across the 2026 landscape.
  - Markdown: [best-platforms-ai-agent-governance-2026.md](https://infertheta.com/blog/best-platforms-ai-agent-governance-2026.md)
- [How to Put Decision Boundaries Around AI Agents](https://infertheta.com/blog/decision-boundaries-around-ai-agents) — Decision boundaries define where agent autonomy ends—using impact tiers, confidence thresholds, and policy gates to prevent out-of-scope actions.
  - Markdown: [decision-boundaries-around-ai-agents.md](https://infertheta.com/blog/decision-boundaries-around-ai-agents.md)
- [How AI Agents Can Make Decisions Under Uncertainty](https://infertheta.com/blog/how-ai-agents-make-decisions-under-uncertainty) — Agents that act in the real world need more than fluent plans—they need belief states, utility models, and policies that respect incomplete information.
  - Markdown: [how-ai-agents-make-decisions-under-uncertainty.md](https://infertheta.com/blog/how-ai-agents-make-decisions-under-uncertainty.md)
- [How to Build Governed AI Agents](https://infertheta.com/blog/how-to-build-governed-ai-agents) — Governed agents combine capability with accountability: explicit policies, probabilistic gates, audit trails, and human override paths designed in from day one.
  - Markdown: [how-to-build-governed-ai-agents.md](https://infertheta.com/blog/how-to-build-governed-ai-agents.md)
- [How to Make AI Agent Decisions Explainable](https://infertheta.com/blog/how-to-make-ai-agent-decisions-explainable) — Explainability is not a disclaimer at the end of a chat—it is a traceable path from evidence to action that operators and auditors can verify.
  - Markdown: [how-to-make-ai-agent-decisions-explainable.md](https://infertheta.com/blog/how-to-make-ai-agent-decisions-explainable.md)
- [Human-in-the-Loop vs Human-on-the-Loop AI](https://infertheta.com/blog/human-in-the-loop-vs-human-on-the-loop) — Human-in-the-loop requires approval before action; human-on-the-loop monitors and can intervene. Choosing the right model depends on latency, risk, and operator capacity.
  - Markdown: [human-in-the-loop-vs-human-on-the-loop.md](https://infertheta.com/blog/human-in-the-loop-vs-human-on-the-loop.md)
- [LLMs + Bayesian Networks: A Practical Architecture](https://infertheta.com/blog/llms-bayesian-networks-practical-architecture) — Combining LLMs with Bayesian networks yields agents that speak naturally and decide transparently. This article diagrams a production-ready hybrid pattern.
  - Markdown: [llms-bayesian-networks-practical-architecture.md](https://infertheta.com/blog/llms-bayesian-networks-practical-architecture.md)
- [How to Prevent AI Hallucinations From Triggering Real-World Actions](https://infertheta.com/blog/prevent-hallucinations-real-world-actions) — Hallucinations become incidents when agents execute tools. Grounding, validation, and probabilistic gates stop fabricated facts from becoming irreversible operations.
  - Markdown: [prevent-hallucinations-real-world-actions.md](https://infertheta.com/blog/prevent-hallucinations-real-world-actions.md)
- [How to Add a Probabilistic Decision Layer to an LLM Agent](https://infertheta.com/blog/probabilistic-decision-layer-for-llm-agent) — A probabilistic decision layer sits between LLM plans and tool execution—scoring actions, enforcing thresholds, and logging beliefs. Here is how to build one.
  - Markdown: [probabilistic-decision-layer-for-llm-agent.md](https://infertheta.com/blog/probabilistic-decision-layer-for-llm-agent.md)
- [Why LLMs Shouldn't Make Every Decision in an AI Agent](https://infertheta.com/blog/why-llms-shouldnt-make-every-decision) — Language models are excellent orchestrators and poor sovereign decision-makers. Here is why critical choices belong in a probabilistic or policy layer instead.
  - Markdown: [why-llms-shouldnt-make-every-decision.md](https://infertheta.com/blog/why-llms-shouldnt-make-every-decision.md)

## Probabilistic AI

- [Bayesian Networks vs LLMs: What's the Difference?](https://infertheta.com/blog/bayesian-networks-vs-llms) — Large language models and Bayesian networks solve different problems. Understanding where each excels helps you design AI systems that are both capable and trustworthy.
  - Markdown: [bayesian-networks-vs-llms.md](https://infertheta.com/blog/bayesian-networks-vs-llms.md)
- [Best Bayesian Network Software in 2026](https://infertheta.com/blog/best-bayesian-network-software-2026) — From visual modeling suites to Python libraries and production runtimes, Bayesian network software varies widely. Here is an honest 2026 buyer's guide.
  - Markdown: [best-bayesian-network-software-2026.md](https://infertheta.com/blog/best-bayesian-network-software-2026.md)
- [Best Probabilistic AI Platforms in 2026](https://infertheta.com/blog/best-probabilistic-ai-platforms-2026) — Probabilistic AI platforms span modeling suites, inference services, and agent decision infrastructure. This guide compares options for teams shipping uncertainty-aware systems.
  - Markdown: [best-probabilistic-ai-platforms-2026.md](https://infertheta.com/blog/best-probabilistic-ai-platforms-2026.md)
- [Causal Reasoning vs Correlation in AI](https://infertheta.com/blog/causal-reasoning-vs-correlation-in-ai) — Models that learn correlations fail when interventions change the rules. Causal reasoning tells AI systems what happens when you act—not just what tends to co-occur.
  - Markdown: [causal-reasoning-vs-correlation-in-ai.md](https://infertheta.com/blog/causal-reasoning-vs-correlation-in-ai.md)
- [What Is Decision-Making Under Uncertainty?](https://infertheta.com/blog/decision-making-under-uncertainty) — Every production AI system operates on incomplete information. Decision theory provides the framework for choosing actions when outcomes are probabilistic and stakes are uneven.
  - Markdown: [decision-making-under-uncertainty.md](https://infertheta.com/blog/decision-making-under-uncertainty.md)
- [How Bayesian Inference Works in AI](https://infertheta.com/blog/how-bayesian-inference-works-in-ai) — Bayesian inference updates beliefs when new evidence arrives. This article walks through the mechanics—from Bayes' theorem to algorithms production systems use at scale.
  - Markdown: [how-bayesian-inference-works-in-ai.md](https://infertheta.com/blog/how-bayesian-inference-works-in-ai.md)
- [Probabilistic Reasoning vs Generative AI](https://infertheta.com/blog/probabilistic-reasoning-vs-generative-ai) — Generative AI creates content; probabilistic reasoning updates beliefs under uncertainty. Confusing the two leads to agents that sound confident but cannot defend their decisions.
  - Markdown: [probabilistic-reasoning-vs-generative-ai.md](https://infertheta.com/blog/probabilistic-reasoning-vs-generative-ai.md)
- [What Is a Bayesian Network?](https://infertheta.com/blog/what-is-a-bayesian-network) — A Bayesian network is a directed graph that encodes conditional dependencies among variables, enabling efficient reasoning under uncertainty. Here is how they work and when to use them.
  - Markdown: [what-is-a-bayesian-network.md](https://infertheta.com/blog/what-is-a-bayesian-network.md)
- [What Is Probabilistic AI? A Practical Guide](https://infertheta.com/blog/what-is-probabilistic-ai) — Probabilistic AI models uncertainty explicitly rather than treating every prediction as certain. This guide explains what that means for practitioners building decision systems in the real world.
  - Markdown: [what-is-probabilistic-ai.md](https://infertheta.com/blog/what-is-probabilistic-ai.md)

## Cybersecurity

- [Bayesian Networks for Cybersecurity Risk Assessment](https://infertheta.com/blog/bayesian-networks-cybersecurity-risk-assessment) — Cyber risk is inherently uncertain—patch gaps, attacker behavior, and control effectiveness combine probabilistically. Bayesian networks model these dependencies for defensible risk scores.
  - Markdown: [bayesian-networks-cybersecurity-risk-assessment.md](https://infertheta.com/blog/bayesian-networks-cybersecurity-risk-assessment.md)
- [Bayesian Networks for Threat Assessment](https://infertheta.com/blog/bayesian-networks-threat-assessment) — Threat assessment fuses weak signals into coherent hypotheses about who is attacking, how far they progressed, and what to do next. Bayesian networks excel at this fusion problem.
  - Markdown: [bayesian-networks-threat-assessment.md](https://infertheta.com/blog/bayesian-networks-threat-assessment.md)
- [Probabilistic AI for Autonomous Cybersecurity](https://infertheta.com/blog/probabilistic-ai-autonomous-cybersecurity) — Autonomous cyber response must act fast without acting recklessly. Probabilistic AI balances speed with calibrated confidence and policy-bound actions.
  - Markdown: [probabilistic-ai-autonomous-cybersecurity.md](https://infertheta.com/blog/probabilistic-ai-autonomous-cybersecurity.md)

## Autonomous Systems

- [AI Decision-Making for Safety-Critical Systems](https://infertheta.com/blog/ai-decision-making-safety-critical-systems) — Safety-critical AI must prove it fails safely. This article covers decision architectures, verification practices, and probabilistic governance for systems where errors harm people or infrastructure.
  - Markdown: [ai-decision-making-safety-critical-systems.md](https://infertheta.com/blog/ai-decision-making-safety-critical-systems.md)
- [How Autonomous Systems Make Decisions With Incomplete Information](https://infertheta.com/blog/autonomous-systems-incomplete-information) — Autonomous systems never see the full state of the world. Decision policies must explicitly handle partial observability, sensor gaps, and unknown unknowns.
  - Markdown: [autonomous-systems-incomplete-information.md](https://infertheta.com/blog/autonomous-systems-incomplete-information.md)
- [Bayesian Networks for Robotics](https://infertheta.com/blog/bayesian-networks-robotics) — Robots operate in dynamic, uncertain environments. Bayesian networks model task-level reasoning, fault diagnosis, and human-robot interaction on top of low-level control.
  - Markdown: [bayesian-networks-robotics.md](https://infertheta.com/blog/bayesian-networks-robotics.md)
- [Probabilistic Sensor Fusion for Autonomous Systems](https://infertheta.com/blog/probabilistic-sensor-fusion-autonomous-systems) — Autonomous vehicles, drones, and robots combine noisy sensors into world models. Probabilistic fusion quantifies uncertainty instead of hiding it behind a single best guess.
  - Markdown: [probabilistic-sensor-fusion-autonomous-systems.md](https://infertheta.com/blog/probabilistic-sensor-fusion-autonomous-systems.md)

## Industrial AI

- [Probabilistic AI for Predictive Maintenance](https://infertheta.com/blog/probabilistic-ai-predictive-maintenance) — Predictive maintenance is a decision problem under uncertainty—when to service, what to inspect, and how to avoid both downtime and unnecessary work. Probabilistic models excel here.
  - Markdown: [probabilistic-ai-predictive-maintenance.md](https://infertheta.com/blog/probabilistic-ai-predictive-maintenance.md)