AI Gateway for Agents: How to Route, Cache, and Govern MCP Workflows
Agents are the fastest-growing segment in AI infrastructure. But no gateway was designed for multi-step workflows. Here's what agent-aware routing looks like.
NR
NeuralRouting Team
April 10, 2026
AI Gateway for Agents: How to Route, Cache, and Govern MCP Workflows
The agent era is here. 78% of enterprises are running AI agent pilots (Gartner, 2026), but only 14% have reached production. The gap isn't in agent frameworks — it's in infrastructure.
Most AI gateways were built for single request-response pairs. Agents operate differently: multi-step workflows, tool calls, accumulated context, compounding costs. This guide explores what an agent-aware gateway looks like and why it matters.
Why Agents Break Traditional Gateways
A typical agent workflow:
Step 1: Plan (orchestration) → needs GPT-4o for reasoning
Step 2: Search (tool call) → no LLM needed
Step 3: Extract data → GPT-4o-mini is sufficient
Step 4: Summarize → Llama 3 is sufficient
Step 5: Generate response → GPT-4o for quality
Traditional gateway approach: every step uses GPT-4o because the agent was configured with a single model.
Cost of 5 steps at GPT-4o: ~$0.05
Cost with per-step routing: ~$0.015 (70% cheaper)
Multiply by thousands of agent executions per day and the savings are massive.
The Three Problems of Agent Infrastructure
1. Cost Accumulation
Single LLM calls are cheap. Agent workflows that chain 5-15 calls are expensive. A modest agent workflow consuming 10K tokens per step across 8 steps = 80K tokens per execution. At GPT-4o rates, that's $1 per execution. At 1,000 executions/day = $30,000/month.
2. Compounding Errors
In a multi-step workflow, each step depends on the previous one. A low-quality response at step 2 can cascade through steps 3-5, producing a completely wrong final output. Traditional gateways have no mechanism to detect this.
3. Budget Unpredictability
Agents make a variable number of LLM calls. A "simple" query might trigger 3 steps; a complex one might trigger 15. Without per-workflow budget controls, costs are unpredictable.
What Agent-Aware Routing Looks Like
Per-Step Model Selection
Instead of one model for the entire agent, each step gets the optimal model:
Step Type
Optimal Model
Cost/1M tokens
Planning/orchestration
GPT-4o
$12.50
Data extraction
GPT-4o-mini
$0.60
Classification
Llama 3.1 8B
$0.20
Summarization
Llama 3.1 8B
$0.20
Code generation
GPT-4o
$12.50
Tool call formatting
GPT-4o-mini
$0.60
A router that classifies each step independently can reduce agent costs by 40-60%.
Cumulative Budget Tracking
workflow_budget = 0.10 # $0.10 max per workflow execution
for step in workflow.steps:
if workflow.spent >= workflow_budget:
# Budget exhausted — return partial result or escalate
return workflow.partial_result()
model = route_by_step_type(step)
result = call_model(model, step.prompt)
workflow.spent += result.cost
This prevents runaway costs from recursive agent loops or unexpectedly complex workflows.
Agent Trace Caching
Many agent workflows are triggered by similar inputs. If an agent executed a similar workflow yesterday:
Cache the intermediate results (step outputs)
On a similar trigger, replay cached steps where input similarity > 0.92
Only re-execute steps where the input differs
This can eliminate 20-30% of redundant LLM calls in agent-heavy applications.
MCP Protocol and Gateway Integration
The Model Context Protocol (MCP) standardizes how agents interact with tools and data sources. An agent-aware gateway should:
Intercept MCP tool calls and route them efficiently
Track token usage per tool for cost attribution
Cache tool responses when appropriate (e.g., database lookups that rarely change)
Rate limit per agent to prevent abuse
The Future: Self-Improving Agent Routing
The next evolution is a gateway that learns from agent execution history:
Which model performs best for each step type in YOUR specific workflows
Which steps can be safely cached vs. which need fresh computation
Which workflows are cost-inefficient and need restructuring
This is where NeuralRouting's Confidence Matrix provides a foundation. By tracking quality scores per (task_type, model) pair, the system accumulates intelligence about optimal routing that transfers across similar agent workflows.
Getting Started
Agent-aware routing is an emerging capability. Today, you can:
Use NeuralRouting as your agent's LLM provider — each step's complexity is classified independently
Set per-session budget limits via the API
Monitor agent costs in the dashboard (per-session breakdown)
The infrastructure for agent-era AI is being built now. The teams that adopt it early will have a significant cost and quality advantage.