AI Agent Guide
Knowledge Guides /How do domain-specific AI knowledge bases differ from task agents, and how should they be accepted?
Last updated: · New Horizon Digital Outsourcing Team
Knowledge-base Q&A primarily answers questions from source materials, while task agents may also call tools and change business state. They require separate acceptance: the former focuses on retrieval, citations, and document access permissions; the latter additionally requires checks of execution permissions, parameter validation, confirmations, failure recovery, and audit records. Connecting a large language model does not establish reliable autonomous decision-making.
Which issues should be addressed first when preparing materials?
- Register material sources, validity periods, versions, and roles permitted to access them. Flag duplicate, conflicting, or superseded content. Before chunking documents and building indexes, confirm which content may be sent to the model or external services.
- Build a benchmark from real business questions, covering answerable and unanswerable questions, cross-document questions, outdated materials, and access-restricted scenarios. Q&A quality cannot be judged by asking a few ad hoc questions just before launch.
How should answers and citations be checked?
- Evaluate separately whether the correct material was retrieved, whether it supports the answer, whether citations locate the relevant source, and whether limitations are stated when information is insufficient. Fluent wording must not conceal incorrect supporting evidence.
- Fix evaluation criteria before comparing configurations, retaining retrieved passages and model versions. When human reviewers disagree, record decision rules rather than substituting a single subjective score for business acceptance.
Tool execution needs additional constraints
- Set different permissions for queries, drafting, and actual submissions. For deletion, payment, publishing, or equipment control, design authorization and confirmation around business risk. Do not let the model execute solely because a document contains an instruction.
- When exceptions occur, clearly distinguish not executed, executed, and unknown status to prevent duplicate submissions. Instructions in external materials must not receive the same authority as user instructions.
Launch and handover checks
- Record applicability, evaluation results, known failure modes, and human takeover procedures according to risk. NIST AI RMF offers a risk-management approach; it is not product certification or an accuracy guarantee.
- Recommended deliverables include data-processing workflows, permission configurations, evaluation sets, index-update methods, and access points for audit records. Rerun key benchmarks after changes to source materials or model versions.