Enterprise AI is evolving fast. Smart assistants can respond to questions, summarize content, and search knowledge bases. That’s useful, but it doesn’t move work forward.
Real value comes from AI that can act. Make decisions. Trigger workflows. Operate like a team member, not a help widget.
Consider the AI agents you’ve seen—chatbots that rely on support articles, copilots that search FAQs, or assistants that summarize documents. Most of these systems only access unstructured data like free text, PDFs, email threads, and sometimes wikis. While they can be helpful, this constrains their potential. Why? Because real enterprise workflows exist in structured systems, not just documents.
These systems are where priorities get set, problems get solved, and customers get support. Most AI tools can’t touch that layer. DevRev can.
We believe that to unlock the true power of Agentic AI, agents need to communicate effectively with both humans and machines.
This means working with structured data systems, including CRM records, support tickets, product telemetry, customer conversations, and backend APIs. Only then can AI agents function less like advanced search engines and more like effective team members.
Most AI agents hit a wall
To illustrate the gap, let’s look at what most Agentic AI implementations do today:
- Train an LLM on a corpus of internal documentation
- Add a vector database for semantic search
- Use RAG (retrieval-augmented generation) to answer questions
- Maybe, if you’re lucky, trigger a simple workflow like “create a Jira ticket”
Useful? Sure. But transformative? Not quite.
This approach fails when agents need to act on real-time information, like checking a customer’s SLA status, escalating a P1 incident, or modifying a product entitlement. These details aren’t found in a wiki; they reside in your structured operational systems. Without structured access, AI agents can’t become true teammates.
The structured data advantage
DevRev’s platform takes a different approach. We created a system designed to make both structured and unstructured data equally accessible to AI agents using a unified graph-based architecture.
A key innovation in DevRev is Airdrop, a bidirectional syncing layer that connects with your entire digital stack, including ticketing systems like ServiceNow or Zendesk, CRMs like Salesforce or Hubspot, GitHub, Jira, WhatsApp, Slack, and more. It continuously supplies both types of data into a near-real-time knowledge graph.
This means DevRev agents can:
- Access a customer’s ticket history, usage telemetry, and entitlements in the same context as their recent support conversations
- Initiate workflows that modify records across multiple systems (e.g., updating CRM and triggering an internal escalation policy)
- Make decisions based on structured logic and unstructured nuance—like a human would, only faster
In short, they don’t just read; they act.
Real results from real use
This isn’t just theoretical. Here are some real-world outcomes from DevRev implementations:
- 75% fewer L1 support tickets as agents handle routine requests on their own.
- 50% faster resolution times with AI triaging and routing issues proactively.
- Up to 40% lower tool costs by unifying support, product, and customer systems in one graph.
These gains don’t come from better document search. They come from agents that work inside your live systems—just like your best operators.
Operational AI is already here
The first wave of AI helped teams understand faster. The next one helps them act faster.
DevRev builds agents that don’t just talk. They take action—across CRMs, support queues, product logs, APIs, and everything in between.
Work doesn’t live in documents. It lives in systems. That’s where real AI needs to operate. And that’s where DevRev agents already do.
Further Reading
- Knowledge graphs: the missing link in enterprise AI | CIO
- Continuous Improvement of RAG Results: Enhancing Performance Metrics
- Every White-Collar Role Will Have An AI Copilot. Then An AI Agent. | Andreessen Horowitz
