A simpler, cheaper, and more secure architecture for enterprise AI
Generative and agentic AI are forcing every enterprise to rethink how their internal systems talk to each other. The Model Context Protocol (MCP) is great because it lets tools plug directly into AI agents. But that still leaves a big architectural question: Do you let every single backend system expose its own agent interface? Or do you bring everything together into one Knowledge Graph and expose a single, unified interface on top of it?
At DevRev, we chose the second path – a Knowledge-Graph-First Architecture – and there’s a reason we’re so confident about it. It’s:
- technically cleaner
- financially smarter
- and far easier to secure and govern
1. Technical simplicity: One graph, Not a hundred interfaces
The problem with “one agent interface per backend system”
If every backend system is independently exposing an MCP endpoint (or anything similar), you’re suddenly managing dozens of independent agent interfaces. That means:
- more inconsistencies
- more places for things to break
- more work keeping everything monitored and audited
Each system will inevitably have its own schema, its own permissions, its own quirks, and its own failure modes. Instead of one clean integration point, you’re dealing with an exponentially growing mess where your integration cost becomes the number of systems × number of exposed agent surfaces.
With a Knowledge Graph, you get the opposite:
One source of truth. One consistent schema. One integration point.
And that dramatically reduces architectural friction – both today and every time you scale.
The Knowledge Graph approach
A Knowledge Graph–first architecture makes life dramatically simpler – both for your data teams and for your AI systems. For years, enterprise data has lived in silos: spread across databases, buried in tools, scattered in documents, and loosely stitched together in data warehouses like Snowflake or Databricks. Even those couldn’t truly connect everything. And unstructured data? That’s usually the first thing left behind.
A Knowledge Graph changes that. Instead of just centralizing data, it connects the dots across your entire enterprise – systems, records, conversations, documents, people, everything. When new data flows in, reconciliation happens right at ingestion, so consistency is built in from day one.
Permissions also become much simpler. Rather than every system reinventing its own rules, you get one unified semantic layer that governs who can see what. And because everything lives in one connected model, grounding AI with the right context becomes straightforward instead of chaotic.
For AI agents, this is huge. They don’t need to talk to twenty different systems – they talk to one. Which means less complexity, fewer integration headaches, and far more reliable outcomes. And since all analytics and reasoning run on the same consistent semantic model, you get smarter, more trustworthy intelligence across the board.
This centralized approach isn’t just our opinion – it’s strongly backed by both industry leaders and academic research. McKinsey repeatedly highlights the advantages of unified data layers, noting that a single architecture helps reduce duplication, simplify operations, and improve how systems work together.
Academia says the same thing. Researchers like Hogan et al. point out that knowledge graphs create a uniform, semantically consistent layer across all your systems, which dramatically cuts down fragmentation.
The takeaway is simple: when you centralize the semantics, you effectively centralize the intelligence. Everything else – governance, scaling, reasoning, security – becomes a whole lot easier.
2. Financial efficiency: One AI layer, not a hundred vendor AI add-ons
When every system exposes its own agent interface, you don’t just get complexity – you also get a serious cost problem. Vendors start layering their own AI “extras” on top: copilots, chatbots, conversation engines, auto-classifiers, retrieval add-ons, LLM extensions… The list keeps growing.
Before you know it, you’re paying separately for Salesforce AI plugins, Zendesk AI add-ons, ServiceNow copilots, Atlassian intelligence packs, SAP and Oracle AI modules each sold as an additional upgrade, each carrying its own price tag. The result? Redundant capabilities, overlapping features, and a very expensive AI bill.
A unified Knowledge Graph completely changes that equation. With a centralized graph, you only need one AI layer. All systems feed into a single model, and your agents reason over one unified representation of the business. No duplicate copilots. No fragmented AI functionality. Just one powerful capability that works everywhere.
Analysts agree. BCG notes that a centralized AI layer “creates significant cost efficiencies by consolidating capabilities that would otherwise be duplicated across business units or platforms.” Deloitte makes the same point: centralizing data and AI reduces redundant spend while improving consistency and control.
So the takeaway is pretty straightforward: one centralized AI layer is not just cleaner – it’s far more financially sensible than managing dozens of scattered AI add-ons.
3. Governance & security: One control plane vs many blind spots
The biggest advantage of a centralized approach is governance. A fully decentralized “MCP everywhere” model may sound flexible in theory, but in practice it becomes a nightmare for security and ops teams. Suddenly they’re managing multiple versions of everything – authentication methods, permission frameworks, audit logs, data exposure points, logging systems, policy engines… all behaving slightly differently across tools. That level of fragmentation is exactly why security leaders are usually wary of highly decentralized architectures.
A Knowledge Graph flips that dynamic. Instead of chasing problems across dozens of systems, you get one consolidated control layer. Permissions are enforced consistently. Data lineage lives in one place. Audit logs are unified. Data exposure is governed through a single, well-controlled chokepoint. Monitoring becomes simpler, and guardrails apply universally across all agent activity.
This isn’t just theoretical preference – it’s backed by real-world research and industry practice. EY highlights that centralized architectures enable consistent policy enforcement, unified lineage, and simpler audits. Academic work – including Wooldridge & Jennings – notes that distributed multi-agent systems are harder to secure, verify, and observe because of their inherent autonomy and diversity. Engineering teams in the field echo this too, pointing out that centralized orchestration makes global error handling, observability, and policy enforcement far simpler.
Why DevRev uses the Knowledge-Graph-first approach
At DevRev, we’ve designed our architecture around a single, centralized Knowledge Graph. Every backend system feeds into it, and one unified semantic model controls permissions, visibility, and access. All agents – whether internal tools or external applications – work through this single interface. We can still support MCP on top of the graph when needed, but it complements the graph rather than replaces it. The result is simple: orchestration, reasoning, security, and monitoring are handled once, across the entire ecosystem.
This decision delivers very real benefits. It dramatically cuts down on the number of integrations you need, eliminates redundant AI add-ons, and reduces operational risk. It lowers costs, improves reliability, and, most importantly, helps every agent perform better because they’re all reasoning over the same trusted source of truth.
In short, this architecture is built to scale, built to govern, and built to consistently deliver value.
