Status: 🟡 In Progress — Deadline August 2026
Stack: vLLM · TensorRT-LLM · LangGraph · MCP · gRPC · Kubernetes · CUDA

What This Is

The infrastructure layer for deploying agentic AI systems in production. Not the agents themselves — the platform that makes them reliable, fast, and observable at scale.

This sits at the emerging boundary between inference serving (get tokens out fast) and agentic orchestration (make reliable multi-step decisions). Most teams treat these separately. This project builds the bridge.

Problem Statement

Deploying a single LLM for generation is a solved problem. Deploying an agent that:

  • Makes tool calls with sub-second decision latency
  • Maintains state across multi-step reasoning chains
  • Fails gracefully when tools return unexpected results
  • Runs concurrently across hundreds of simultaneous sessions
  • Is observable enough that you can debug production failures

…is not solved. This project is building that infrastructure.

Architecture

┌─────────────────────────────────────────────────────────┐
│                   Agent Request Layer                    │
│              gRPC API · Session Management               │
└─────────────────────┬───────────────────────────────────┘
                      │
┌─────────────────────▼───────────────────────────────────┐
│               LangGraph Orchestration                    │
│         State Machine · Tool Registry · Retry Logic      │
└──────────┬──────────────────────────┬───────────────────┘
           │                          │
┌──────────▼──────────┐   ┌───────────▼───────────────────┐
│   vLLM Inference    │   │        MCP Tool Server         │
│  (token generation) │   │  (structured tool execution)   │
│  Multi-GPU · KV     │   │  Yahoo Finance · Code Exec ·   │
│  Cache Paging       │   │  Database · Custom APIs        │
└──────────┬──────────┘   └───────────────────────────────┘
           │
┌──────────▼──────────────────────────────────────────────┐
│              Observability Layer                         │
│        Prometheus · Grafana · Distributed Tracing        │
└─────────────────────────────────────────────────────────┘

Key Technical Challenges

1. KV-Cache Management for Long Agent Sessions

Multi-step reasoning chains can span dozens of turns. Standard KV-cache strategies waste memory on completed reasoning steps. Solution: prefix caching + aggressive eviction for completed tool call branches.

2. Concurrent Session Isolation

100 simultaneous agent sessions shouldn’t interfere with each other’s state. Building session-isolated execution contexts with shared inference backend.

3. Tool Call Reliability

When an MCP tool returns an error, the agent needs to decide: retry, use fallback, or fail cleanly? Building a typed error taxonomy so agents can reason about failures.

4. Latency Budget Management

Each reasoning step has a time budget. If tool calls exceed it, the agent needs to degrade gracefully (use cached results, skip tool, etc.) rather than hang.

Benchmarks (Ongoing)

MetricTargetCurrent
Tool call round-trip p50< 200ms—
Tool call round-trip p99< 500ms—
Concurrent sessions100+—
Agent decision latency p50< 1s—
Throughput500+ req/s—

Will update as benchmarks complete.

Milestones

  • M1 (March 2026): Single-agent session, vLLM + LangGraph + 3 MCP tools working end-to-end
  • M2 (May 2026): Concurrent session handling, observability stack, baseline benchmarks
  • M3 (July 2026): Production hardening — error handling, graceful degradation, load testing
  • M4 (August 2026): Full benchmark suite, architecture writeup, public repo

Posts will appear here as they’re published.

GitHub: Available August 2026