Moving Beyond LLM Hallucinations in Network Architecture Planning

Estimating AWS Direct Connect (DX) requirements is a task characterized by rigid mathematical constraints and unforgiving physical limits. In traditional workflows, an architect manually balances bandwidth needs against redundancy models—often choosing between 1:1 or N:1 architectures—while cross-referencing AWS documentation to avoid hitting regional capacity caps or VIF limitations.

The risk isn't just getting the math wrong; it's designing a deployment that is physically impossible once you hit the provisioning stage. You might design a perfect redundant loop on paper, only to realize later that your chosen edge location cannot support the number of 1 Gbps connections you’ve specified.

With the rise of AI agents acting as co-pilots for infrastructure engineering, we face a new problem: Large Language Models (LLMs) are notoriously bad at deterministic combinatorial logic involving specific hardware constraints. They excel at describing what a Direct Connect connection is, but they struggle with exactly how many physical ports you need to satisfy a specific uptime SLA across multiple regions.

To bridge this gap, we built the AWS Direct Connect Bandwidth Calculator, a specialized connector designed to provide deterministic answers where reasoning engines usually fail.

The Problem of Determinism in Agentic Workflows

When you ask an LLM to 'plan a redundant network connection,' it relies on probabilistic patterns learned during training. It understands the concept of redundancy, but it doesn't perform real-time validation against current AWS service quotas or physical port availability unless specifically directed to use a tool designed for that purpose. This leads to the 'hallucinated architecture'—a plan that looks technically sound in prose but violates fundamental AWS constraints.

The calculator addresses this through three highly specific tools exposed via the Model Context Protocol (MCP):

1. calculate_connection_requirements: Instead of guessing, the agent uses this tool to determine core physical connection counts based on input parameters like traffic demand (Gbps), target number of regions, selected redundancy model (1:1 vs N:1), and individual connection speeds.
2. get_mtu_configuration: Eliminates errors regarding packet sizes by providing exact MTU settings relative to the connection type (such as identifying 8500 bytes when jumbo frames are enabled).
3. validate_architectural_limits: This is perhaps the most critical component. It acts as a guardrail, checking whether a proposed plan violates AWS physical and logical boundaries, such as maximum connections per location or regional capacity limits.

A practical failure case often involves attempting to deploy twenty 1 Gbps links within a single location. While logically possible in some contexts, many locations carry hard caps—for instance, limiting a single location to four 1 Gbps connections. Without validate_architectural_limits, an agent might confidently suggest this configuration; with it, the agent is forced to acknowledge the violation immediately.

Engineering Reliability into Connectivity

Building these kinds of technical connectors requires more than just wrapping an API or running a script. As I worked on developing MCPFusion—the open-source TypeScript framework used to build all Vinkius connectors—the focus was always on consistency and predictable behavior across different MCP clients like Claude or Cursor.

A significant hurdle in moving from 'local testing' to 'production agency' is managing the interface between the AI and the underlying system. Most developers spend far too much time wrestling with OAuth flows or securing local environments just to give an agent permission to run a calculation or query a database.

Vinkius solves this by treating connectivity as a managed layer rather than a collection of fragmented scripts. When using our AWS Direct Connect connector, you aren't managing individual credentials for every microservice or utility. You use a single gateway and one connection token provided through our catalog. This removes the friction of constant reconfiguration that typically kills momentum during complex engineering tasks.

Furthermore, giving an AI agent 'write' access or even heavy 'read' access to infrastructure tooling introduces massive security concerns. At Vinkius, we treat governance as an architectural requirement rather than an afterthought. Every connector operates within an isolated V8 sandbox and is governed by eight distinct policies, including Data Loss Prevention (DLP) and SSRF prevention. If an agent attempts to use information gained from one tool call in an unauthorized way elsewhere, our HMAC audit chains and kill switches are designed to intercept that action.

Practical Application: Designing Redundant Links

You can interact with these tools using natural language prompts that translate into structured tool calls. Here are examples of how the precision manifests:

• Requirement Calculation: If you prompt, "I need 10 Gbps of bandwidth across 2 regions with 1:1 redundancy using 10 Gbps connections," the agent won't guess your multiplier. It calculates that while you need 2 connections for base throughput, satisfying 1:1 redundancy necessitates exactly 4 total connections.
• Constraint Validation: Asking "Check if my plan of 20 connections for 1 Gbps links in 1 region violates AWS limits" yields an immediate corrective response: acknowledging that most single locations are capped at 4 connections for that specific tier.
• Configuration Accuracy: Requesting MTU details for jumbo frames returns precisely 8500 bytes, preventing downstream fragmentation issues in your VPC transit gateways.

The goal here isn't just to make AI 'smarter.' It is to augment existing professional expertise with tools that enforce accuracy through strict protocol adherence.\

---

AI agents only matter when they reach real systems. We built the connector catalog. Discover Vinkius.