Autonomous Agent Swarms

Multi-agent orchestration with mathematical guardrails

Design and deploy self-correcting LangGraph workflows, deterministic tool-calling pipelines, and supervisor-governed state machines that maintain enterprise auditability while scaling agentic reasoning.

Multi-agent LangGraph workflows with supervisor consensus
Self-correcting finite-state machines with rollback semantics
Deterministic tool calling with schema-validated I/O
RAG-augmented agent memory via pgvector retrieval
Sliding-window rate limiting and observability hooks

Case Studies

Enterprise Document Intelligence Pipeline

Fortune 500 Financial Services

The Challenge

Manual review of 12,000+ regulatory filings per quarter created 6-week bottlenecks and inconsistent compliance annotations across distributed teams.

Control & Agent Architecture

Supervisor LangGraph with research, extraction, and compliance agents operating on a shared state machine. Self-correction loops validate schema outputs against Zod contracts before supervisor approval gates release.

Hardware / Software Stack

LangGraphOpenAI / Anthropic via Vercel AI SDKSupabase pgvectorUpstash RedisNext.js Route Handlers

Measured ROI

87% reduction in review cycle time; $2.4M annual operational savings with 99.2% annotation consistency.

Case study · enterprise-doc-intelligence

Multi-Agent Code Review Swarm

Series B SaaS Platform

The Challenge

Engineering teams lacked scalable architecture review capacity, resulting in security regressions shipping to production despite manual PR reviews.

Control & Agent Architecture

Parallel agent swarm (security, performance, architecture) with deterministic tool-calling to static analysis APIs. State machine enforces mandatory remediation before merge approval.

Hardware / Software Stack

CrewAILangGraphGitHub API toolsSemgrep integrationFastAPI backend

Measured ROI

64% fewer production incidents; 3.2× increase in review throughput without additional headcount.

Case study · multi-agent-code-review