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SYSTEMS LAB 04 Model Context Protocol (MCP) Compounding Context Economics

Agentic Workflow & MCP Architecture Lab

Interactive architecture simulator for autonomous LLM agent systems. Model multi-agent topology communication, Model Context Protocol (MCP) JSON-RPC 2.0 wire overhead, quadratic context token burn across multi-turn reasoning loops, and deterministic guardrail cost envelopes.

Workflow & Agent Configuration

Topology parameters, tool call density & caching model
ReAct Loop
Single ReAct
Thought → Action → Obs
Orchestrator
Hub & Worker Spoke
Peer Swarm
Consensus / Hand-offs
DAG Pipeline
Sequential Stages
$3.00 / $15.00 per 1M
6 Turns
1 Turn (Direct) 10 Turns (Medium) 20 Turns (Autonomous Swarm)
3,500 Tokens
System instructions, JSON tool schemas (Model Context Protocol), and behavioral guardrails.
1,200 Tokens
Token payload returned by MCP tools (SQL result sets, browser HTML DOM, file view outputs).
80% Reused
Percentage of prior turns and system instructions served from cached KV RAM (up to 90% cost reduction).

Systems Telemetry & Economics

Compounding context burn, cumulative cost & execution wall-clock
Optimal Budget
Cumulative Context Burn
44,700
Total tokens across 6 turns
Final Turn Context Length
11,300 tok
Context window footprint
Cost per Task Run
$0.0482
Saved $0.086 via prompt caching
10k Runs Monthly Budget
$482.00
Enterprise production run-rate
Compounding Context Accumulation Per Turn Cached vs. New Tokens
Model Context Protocol (MCP) Wire Frame JSON-RPC 2.0

Multi-Agent Architectures Compared: Tokens, Latency & Failure Modes

Topology Pattern Context Compounding Fault Isolation Token Efficiency Wall-Clock Latency Best Production Use Case
Single ReAct Loop Quadratic $O(N^2)$ accumulation Low (Single loop failure derails task) High for short tasks (≤5 turns) Sequential ($N imes ext{step}$) CLI tools, data queries, file edits
Orchestrator-Workers Linear $O(N)$ (Subagents branch context) High (Worker errors trapped & retried) Very High (Isolated subagent histories) Parallel speedup on branched steps Complex codebase refactors, research
Autonomous Swarm Linear-Quadratic with hand-offs Medium (Runaway loops require hard caps) Variable (High burn without pruning) High (Inter-agent communication overhead) Peer review, adversary red-teaming
Pipeline DAG Strictly bounded (Structured hand-off) Maximum (Stage checkpoints & rollback) Maximum (Only summaries passed forward) Deterministic per stage pipeline Document ingestion, ETL, compliance

Foundations & Production Architecture for Autonomous Agents