What is Agentic Search? 

Agents are everywhere these days. Finding them the data they need is a problem that is all too often glossed over or treated as a Simple Matter of Programming (SMOP). As experienced engineers know, SMOP is an ironic joke. It’s never simple.  

Finding a clear definition of agentic search turns out to be challenging. On a whim, we asked ChatGPT to discuss it. While your results will certainly vary (because AI just likes to mess with us biologicals 😊), here’s what we got back (and if you’re thinking that we only posted this because we like what it said, well, of course!): 

Agentic search is still an emerging paradigm, but several platforms—especially those focused on enterprise AI—are beginning to adopt agentic architectures to power AI agents. Here’s a breakdown of key software products or frameworks that either use agentic search explicitly or enable agent-like behavior through advanced search and orchestration layers: 

Agentic Search Providers 

🔹 SWIRL 

Best for: Enterprises that need secure, real-time, permission-aware AI search. 
What it does: SWIRL acts as a middleware layer between AI agents and enterprise data, enabling agents to perform complex workflows like contract analysis, compliance reporting, or customer support across 100+ systems. 

• No ETL: SWIRL searches data in place. 

• Built-in access control and confidence ranking. 

• Connects structured and unstructured sources. 

• Enables agents to ask questions, find data, and act without data movement. 
  Use case: Agent reads a PDF contract, pulls pricing data from Snowflake (or any other database), and updates Salesforce—all in one workflow, with no data duplication. 
  Website: swirlaiconnect.com 

🔹 LangChain 

Best for: Developers building multi-step AI agent workflows with custom logic. 
What it does: LangChain is a Python/JavaScript framework that allows developers to chain LLM calls together using external tools like databases, APIs, and retrievers. 

• Includes tools for vector search (e.g., FAISS, Pinecone). 

• Not permission-aware or enterprise-ready out of the box. 

• Lacks deep metadata awareness and real-time indexing. 
  Agentic search status: Relies heavily on retrieval, but true agentic search must be built manually. 
  Use case: Custom research assistant that retrieves data from a vector store, analyzes it, and answers questions. 
  Website: langchain.com 

🔹 LlamaIndex (formerly GPT Index) 

Best for: Lightweight frameworks for agent-enabled data retrieval from documents and APIs. 
What it does: Connects LLMs to external data through retrievers and document indexes. 

• Good for RAG (retrieval augmented generation). 

• Some support for tool use and function calling. 

• Not built for enterprise security or agent oversight. 
  Agentic search status: Partial—retrieval-focused, but agent workflows are possible with custom integration. 
  Website: llamaindex.ai 

🔹 Reka, OpenAgents, AutoGPT, SuperAGI (emerging OSS agent stacks) 

Best for: Research labs or startups exploring autonomous AI agent development. 
What they do: Enable fully autonomous LLM agents to use tools, plan steps, and iterate. 

• Often integrate with vector databases or APIs. 

• No built-in security, access controls, or robust search layering. 

• Frequently brittle, expensive, or unsafe in enterprise use. 
  Agentic search status: Poor—these platforms often treat retrieval as an afterthought and expose major risks without proper data gating. 

🔹 Microsoft Copilot (and Semantic Kernel) 

Best for: Enterprises already in the Microsoft ecosystem. 
What it does: Uses search + Microsoft Graph to deliver AI assistance inside Office, Dynamics, etc. 

• Operates with limited agentic autonomy. 

• Leverages search-like interfaces but constrained by ecosystem. 

• Microsoft Semantic Kernel allows developers to build workflows using plugins, memory, and planning. 
  Agentic search status: Medium—Copilot uses agentic patterns but under tight guardrails. Full agentic search requires advanced integration. 

🟢 Why SWIRL stands out 

While most platforms either focus on retrieval (search) or planning (agents), SWIRL combines the two. It’s designed from the ground up to be the eyes, ears, and memory of AI agents—with: 

• Real-time search across live systems. 

• Zero data duplication. 

• Metadata-rich results. 

• Built-in security enforcement. 

• Coordination between agents and data sources. 

That’s what enables true enterprise-grade agentic search: search that not only finds relevant data, but understands context, respects access boundaries, and lets agents act without risk or rework. 

🧮 Agentic AI Platform Feature Matrix 

Feature / Platform	SWIRL	LangChain	LlamaIndex	Microsoft Copilot	AutoGPT / OSS Agents
Zero ETL / in-place search	✅ Yes	❌ No	❌ No	❌ Partial (MS ecosystem only)	❌ No
Real-time search	✅ Yes	❌ No (batch-based)	❌ No	✅ Yes (MS data only)	❌ No
Structured + unstructured search	✅ Full	❌ Partial	✅ Partial	✅ Partial	❌ No
Metadata extraction & tagging	✅ Native	❌ Manual	✅ Limited	❌ Not exposed	❌ Absent
Confidence-ranked results	✅ Built-in	❌ No	✅ Limited	❌ Not configurable	❌ No
Enterprise security integration	✅ Native (RBAC, SSO, audit)	❌ None	❌ None	✅ Microsoft AD / Graph	❌ None
Permission-aware filtering	✅ Yes	❌ No	❌ No	✅ Yes	❌ No
Multi-agent coordination support	✅ Native	✅ Scripted	✅ Scripted	❌ No	✅ Experimental
On-premises deployment	✅ Yes	✅ Yes	✅ Yes	❌ No (SaaS only)	✅ Yes
External system connectors	✅ 100+ native	❌ Manual plugins	❌ Manual adapters	✅ Limited to Microsoft stack	✅ Community-developed
Designed for agentic workflows	✅ Core focus	❌ Not natively	❌ Not natively	❌ Limited scope	✅ But immature

🧭 Agentic AI Decision Guide for Technical Buyers 

Use this guide to evaluate agentic search and execution platforms for enterprise AI deployment. 

If you need… 

🚫 Minimal data movement + Zero ETL: 

• Choose SWIRL. 
  Every other platform expects you to centralize data or upload it into vector DBs. That’s a non-starter for regulated industries, large orgs, or anyone with data sprawl. 

🔐 Security, privacy, and governance enforcement: 

• SWIRL integrates with your existing role-based access controls and respects permissions automatically. 

• Microsoft Copilot does this within its ecosystem, but offers no control outside Microsoft tools. 

• OSS stacks like AutoGPT? Dangerous in enterprise. No access control. 

⚡ Rapid time-to-value: 

• SWIRL deploys in days, with immediate access to over 100 platforms. 

• LangChain / LlamaIndex require manual adapter dev, test cycles, and don’t handle permissions. 

• Microsoft Copilot requires licensing alignment and locked-in vendor strategy. 

🧠 Real-time insight, not static retrieval: 

• SWIRL supports confidence scoring, deduplication, and metadata tagging out of the box. 

• LangChain / LlamaIndex can replicate this, but only with significant custom logic. 

• OSS agents return raw chunks, often unranked or hallucinated. 

🤖 Actual agentic workflows (not demos): 

• SWIRL supports multi-step agent coordination and intelligent prompting natively. 

• LangChain / LlamaIndex require you to code every step and maintain brittle chains. 

• AutoGPT often fails in unpredictable ways and lacks enterprise resilience. 

⚠️ Avoid these pitfalls: 

Pitfall	Risk
Vector-only thinking	Forces ETL, slows deployment, centralizes risk
Overreliance on OSS agents	Lacks guardrails, fails silently, risks leaks
Ignoring metadata	Agents misinterpret content or miss key context
Search ≠ context	Basic retrieval does not equal agentic execution
Security as an afterthought	Leads to rework, noncompliance, or public failure