Automatan gives AI agents the ability to determine what knowledge they need, discover it across enterprise systems, evaluate the evidence, refine their search, and apply grounded context to complex business workflows.
Dynamic retrieval planning · Multi-source validation · Grounded reasoning
Traditional RAG retrieves documents related to a user query. Enterprise work requires agents that understand the objective, determine what must be known, investigate multiple sources, evaluate conflicting evidence, and continue retrieving until the context is sufficient to act.
Conventional retrieval searches for content similar to the words a user provides. It can return relevant passages without understanding whether they are sufficient to solve the underlying business problem.
Critical answers rarely exist in one document. They emerge from contracts, databases, policies, applications, operational records, external sources, and the relationships between them.
Retrieval agents decide where to search, which methods to use, how to assess evidence, and when additional investigation is required. Retrieval becomes an autonomous reasoning process—not a single search operation.
Automatan orchestrates specialized retrieval and reasoning agents through a dynamic knowledge discovery process. Each stage improves the relevance, completeness, and reliability of the context supplied to downstream agents.
The agent interprets the business objective, user intent, domain context, required output, and decision criteria. It translates an open-ended request into explicit knowledge requirements.
The agent decomposes the objective into research questions, identifies information gaps, selects appropriate sources, and determines which retrieval methods and tools should be used.
Retrieval agents investigate documents, databases, applications, APIs, knowledge bases, websites, and structured systems using semantic search, vector retrieval, keyword search, structured queries, knowledge graph traversal, and controlled web research.
Agents evaluate each result for relevance, authority, freshness, completeness, and consistency. Contradictions and missing evidence trigger new retrieval paths rather than being silently passed downstream.
Validated evidence is connected, ranked, and assembled into a task-specific context package. Reasoning agents use that context to generate conclusions, support decisions, or pass grounded instructions into an automated workflow.
Automatan combines autonomous retrieval, reasoning, orchestration, and validation capabilities into one enterprise-grade knowledge infrastructure.
Transform complex business objectives into focused research questions, knowledge dependencies, and executable retrieval tasks.
Select sources, search methods, tools, and retrieval sequences based on the objective, domain, and evidence discovered during investigation.
Combine semantic search, vector retrieval, keyword search, structured queries, APIs, applications, and external research within a single retrieval workflow.
Identify relationships between entities, events, policies, products, customers, suppliers, and operational dependencies that isolated documents cannot reveal.
Evaluate retrieved evidence, detect weak coverage or contradictions, reformulate queries, and continue searching until defined quality thresholds are reached.
Build task-specific context from validated sources, with attribution, freshness signals, confidence indicators, and unresolved information gaps preserved.
Agentic RAG does not treat retrieval volume as retrieval quality. Automatan continuously assesses whether available evidence is relevant, current, credible, consistent, and sufficient for the decision or workflow ahead.
Measure how directly each source supports the objective—not merely how closely it resembles the original query.
Prioritize approved systems, authoritative records, trusted publishers, and domain-specific sources according to enterprise policies.
Identify superseded documents, outdated records, and newer evidence before information enters the reasoning context.
Compare claims across sources, surface disagreement, and trigger additional investigation when the evidence is inconsistent.
Trace queries, sources, rankings, evaluation decisions, refinement loops, and evidence coverage across the complete retrieval process.
A business objective becomes a structured investigation with explicit evidence, refinement, and grounded action.
The agent identifies the required decision: whether the disruption materially threatens production, customer commitments, regulatory obligations, or financial performance.
The investigation requires supplier performance records, active contracts, inventory and production systems, quality incidents, customer delivery commitments, and external supplier announcements.
The supplier notice conflicts with internal delivery data. The agent retrieves allocation-level inventory, alternative supplier qualification records, affected customer orders, and contractual escalation provisions.
The disruption creates a nine-day production exposure across two regulated product lines. Recommended action: initiate contractual escalation, accelerate the qualified secondary supplier, and notify affected account and quality teams.
Role-aware access controls, encrypted data handling, source-level permissions, and policy-aligned retrieval protect sensitive enterprise knowledge.
Evidence evaluation, retrieval refinement, contradiction detection, and source attribution improve the dependability of agent-generated outputs.
Enterprise information remains governed by defined access, retention, isolation, and processing requirements across the retrieval lifecycle.
Integrate Agentic RAG with existing applications, data systems, knowledge platforms, models, and security architecture.
Give every enterprise agent the ability to determine what knowledge it needs, find it across the right sources, validate the evidence, and apply grounded context to real business work.
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