# How Kognitos Uses Neurosymbolic AI Source: https://www.kognitos.com/platform/neurosymbolic-ai/ Home/Platform/Neurosymbolic AI Definition # What is neurosymbolic AI? Neurosymbolic AI combines neural networks with symbolic knowledge and reasoning. Kognitos applies this approach through AI-assisted interpretation and deterministic execution of approved English rules. Input accuracy, permissions and connected-system outcomes still need validation. Start Building Free Book a Demo ## Neurosymbolic AI, defined Neurosymbolic AI, also written neuro-symbolic AI, brings together neural learning and symbolic representations or reasoning. Neural networks recognize patterns in language and other data; symbolic methods represent knowledge and apply explicit relationships or rules. IBM Research describes the broader field. Different systems combine these methods differently; the category does not prescribe one runtime or guarantee error-free results. For an invoice workflow, reading a document and enforcing a payment policy are different tasks. An extracted amount can be wrong even when the subsequent calculation follows its rules correctly. Evaluate the source fields, the governing rule and the actual action together. ## How Kognitos applies the approach - Interpret and prepare. AI helps interpret language and documents. Review the resulting procedure and validate source or model-derived values before relying on them. - Execute approved rules. Kognitos's symbolic executor runs approved English-as-Code logic deterministically. The no-LLM-inference claim applies to that execution layer; planning, exception handling and explicit model steps must be considered separately. - Inspect and resolve. Review the input, procedure and run evidence when an exception occurs. A resolution for one case and instructions intended for later cases are separate decisions. Repeatability depends on the same rule version, inputs and relevant system state. Changed source records, model outputs or external services can change a workflow's result. A completed automation alone does not establish that an ERP accepted a posting. ## How should buyers compare LLM, RPA and neurosymbolic systems? Compare current products on the same task. LLM-based applications can call deterministic tools, and automation platforms can combine document processing, APIs and human review. Broad labels do not establish which system will handle your workflow best. UiPath documents escalation resolutions that can be retained and reused through Agent memory. Sema4 describes natural-language Runbooks and SQL-powered DataFrames for calculations. These are vendor-documented capabilities, checked October 26, 2026; they are not independent performance tests. Kognitos's approach centers on approved executable English and inspection of the work. Ask each provider to show the exact rule, who can change it, how a reusable resolution is reviewed, and which record proves the final outcome. Use identical inputs, a rule change, an applicable exception and a boundary case before drawing a comparison. ## What evidence should an invoice evaluation show? Start with the six-case invoice evaluation, which supplies fictional inputs and separates expected decisions from actual results. Capture the original invoice, purchase order and receipt; checked extracted values; the approved procedure; any human decision; and the actual ERP response. Its sample files are test fixtures, not proof of a connected Kognitos run. For exception reuse, show the original exception and resolution, the reviewed guidance, a later run where it applies, and a case where it must not. The Troubleshooting Guide documents suggested guidance, Add to Guide or Discard, and Applied to Runs. Confirm the actual review controls and reuse scope in your environment; do not assume that accepting guidance satisfies every approval policy. Explore the platform architecture and the exception-handling guide for supporting explanations. Evaluate integrations, access and evidence requirements for the selected workflow before release. FAQ ## Neurosymbolic AI, answered ### How is neurosymbolic AI different from a large language model? A large language model learns statistical patterns to generate outputs. Neurosymbolic systems combine neural methods with symbolic knowledge or reasoning. Their designs vary, and LLM applications can also call rule engines or other deterministic tools. Compare the actual interpretation, execution and review mechanisms. ### Does neurosymbolic AI eliminate every kind of error? No. In Kognitos, deterministic execution of approved English rules is distinct from AI interpretation and external-system behavior. Incorrect extracted values, incomplete rules, permissions or failed integrations can still produce errors. Validate the input and inspect the actual outcome as well as the rule. ### How can an invoice exception become reusable guidance? Kognitos documents saving resolution instructions in its Troubleshooting Guide and reviewing suggestions with Add to Guide or Discard. Accepted guidance can apply to later matching exceptions, with affected runs available for inspection. Test both an applicable case and a case outside the intended scope before relying on reuse. ### Is neurosymbolic AI suitable for regulated industries? Explicit rules and inspectable execution can support controls in regulated workflows. Suitability depends on the configured process, permissions, data handling, review and evidence requirements. An architecture alone does not establish regulatory compliance or certification. ## See neurosymbolic AI in action. Evaluate one workflow with source inputs, approved English rules, exception review and an inspectable result. Book a Demo Or try it free → ## Source links - IBM Research describes the broader field: https://research.ibm.com/topics/neuro-symbolic-ai - UiPath documents escalation resolutions that can be retained and reused through Agent memory: https://docs.uipath.com/agents/automation-cloud/latest/user-guide/agent-escalations - Sema4 describes natural-language Runbooks and SQL-powered DataFrames for calculations: https://sema4.ai/learning-center/enterprise-ai-agents-vs-rpa/ - Troubleshooting Guide: https://docs.kognitos.com/guides/platform/automations/troubleshooting-guide