From Single‑Turn Chat to Enterprise‑Grade AI Search Autopilot: Introducing Agentic Search 2.0

Overview

Agentic Search 2.0 is an enterprise‑level AI search agent designed for long‑running, cross‑data‑source, cross‑tool tasks. It aims to deliver traceable, auditable, and deliverable results by integrating search, reasoning, execution, permission governance, and memory into a closed‑loop production capability.

Key Challenges for Enterprise Agents

Complex tasks lack reliable orchestration, making sub‑task dependencies, loops, intermediate states, and failure recovery hard to manage.

Heterogeneous data (PDF, images, tables, video, databases) cannot be uniformly parsed.

Long‑term memory is fragmented; multi‑source memories conflict and lack provenance.

Multi‑tenant permission, tool permission, prompt‑injection protection, and execution audit are incomplete.

Integration of external APIs, databases, RPA, email, and IM systems lacks a unified abstraction, leading to brittle pipelines.

Agents answer generic questions but miss business‑specific rules, constraints, and multi‑objective considerations.

From L2 to L4 Autopilot

Earlier single‑turn conversational agents resemble L2 driver assistance—human issues a command, the system executes one step, then waits. Agentic Search 2.0 targets L4 autonomy: the user sets goals and constraints, and the agent autonomously perceives, plans, executes, and delivers, handling failures and recovery on its own.

Dual‑Chain Architecture

The system consists of two main chains—"Search Intelligence" and "Agent Execution"—linked by a unified Orchestration layer. The search side performs multimodal document parsing, read‑only reuse of existing Elasticsearch/Vector indices, Memory, PageIndex, Wiki, four‑layer retrieval, web search, RRF weighting, re‑ranking, conflict resolution, and citation marking. The execution side includes a Harness main agent and vertical SubAgents that manage DAG scheduling, state tracking, result aggregation, MCP, Skills, sandboxed execution, reasoning graphs, execution plans, full‑traceability, permission injection, multi‑tenant isolation, and audit governance.

Production‑Grade Implementation

Dynamic DAG orchestration is required; deterministic automation handles steps unsuitable for LLM planning. The system decides when to invoke a model versus a fixed tool, filters out irrelevant tools, and releases resources when parent tasks are cancelled. In cloud deployments, hundreds of concurrent long‑running tasks demand resource scheduling, elastic scaling, history, memory, and trace retention. SubAgent Matrix can combine up to 30‑50 parallel SubAgents for multimodal search, knowledge‑base recall, smart crawling, Skills, and tool management.

Large‑document processing includes OCR for scanned pages, handwriting, and complex tables, followed by semantic understanding, table structuring, and content recomposition into TXT, Markdown, JSON, or tabular formats. In a "Three Kingdoms"‑size PDF test, PyMuPDF parsing stays under 4.8 seconds. Pre‑processing handles extreme scans, garbled files, auto‑scaling, error correction, isolated region cutting, table region extraction, and popup separation.

PageIndex provides hierarchical indexing to preserve document structure, enabling chapter, paragraph, and page location with path and summary generation; although it adds index‑building time, it prevents information fragmentation in ultra‑long documents.

Hybrid retrieval merges knowledge‑base, user files, databases, and web sources. Vector and keyword recall feed a hybrid pipeline that undergoes re‑ranking, permission filtering, citation validation, and finally produces summaries, insights, citations, and next steps. The four‑layer retrieval stack (Memory → KNN/BM25 → PageIndex → ES/SQL) achieves >90 % recall accuracy on internal test sets and can connect to 50+ data sources.

Existing Elasticsearch indices are reused in read‑only mode; multimodal parsing, indexing layers, and orchestration are added on top without requiring data migration, reducing cost and risk.

Code execution runs inside an isolated Python sandbox that auto‑pulls dependencies, isolates resources, and allocates on demand. Results are stored as temporary files, encrypted, uploaded to OSS, and accessed via signed URLs for rendering and packaging. The sandbox also enforces RASP anti‑injection, dual‑stage query/chunk interception, physical and logical data isolation, main‑process sandboxing, memory‑overflow protection, and system‑level audit. Security is baked into every stage—from retrieval through tool calls, code execution, and asset delivery.

Memory, Skill and Continuous Evolution

Agentic Memory transforms one‑off executions into reusable capabilities. It merges long‑term, local, knowledge‑base, user‑preference, and external‑knowledge recalls, de‑duplicates, re‑ranks, and fuses System Prompt, dialogue history, retrieved memory fragments, and user input into enriched context, producing high‑relevance, non‑redundant, traceable memory snippets. After task completion, key facts and successful paths are asynchronously written back for continual update.

Memory is a composite of short‑term session memory, cross‑session long‑term memory, device‑local memory, knowledge‑base, and user profile. When conflicts arise, merging and re‑ranking resolve them. Successful task paths are extracted as Skills, which include I/O definitions, parameter validation, versioning, permission control, scheduling, and effect evaluation, making them repeatable task abilities.

The system separates model‑generated code from deterministic execution: the LLM generates code, the code produces structured data, and the data is embedded into the final report, reducing numeric volatility. The end‑to‑end report pipeline starts with large PDF upload, proceeds through rapid parsing, mixed recall, structured report generation, and OSS packaging for download.

Real‑World Case: Consumer‑Electronics Research

In a consumer‑electronics knowledge base covering 100+ countries, the system stores ~2 GB of 3,000 industry research documents, ~1.5 GB of 500,000 VOC comments, and ~1.5 GB of 2,000 consumer‑insight documents. Monthly calls reach 10,000 with peak concurrency of 50 QPS.

Before deployment, a deep‑research report required 3‑5 working days, with >30 % of time spent on cross‑system lookup and manual collation. After deployment, a report is generated in hours, with information‑lookup time dropping below 10 % and ChatBI dashboards rendering in seconds. Quality is ensured by five‑channel parallel recall, Memory‑Aware filtering, and multi‑objective re‑ranking; assets are unified into a searchable, reusable, traceable repository where citations map to report page numbers and shared memory reduces duplicate effort.

Takeaways

Enterprise‑grade AI search agents succeed not by delivering a single fluent answer but by reliably completing long‑running, multi‑source, multi‑tool tasks while preserving traceability, auditability, and reusable capabilities through a tightly coupled search‑execution‑memory loop.