AUTONOMOUS AI AGENTS & WORKFLOW AUTOMATION

Sentinel AI Agent Fleet

Enterprise multi-agent autonomous system streamlining customer intelligence, incident triage, and real-time operations.

DISCIPLINES & SERVICES

·LLM Integration
·Agentic Workflows
·Model Fine-Tuning
·Real-Time Telemetry Pipeline

Standard LLMs produce hallucinations and lack strict deterministic guardrails. Sentinel needed an autonomous system that could be trusted to make operational API calls, parse complex log formats, and adhere strictly to enterprise compliance guidelines.

  • High hallucination risk when handling edge-case system logs and customer queries.
  • Latency constraints requiring response generation under 150 milliseconds.
  • Data privacy mandates prohibiting sensitive customer PII from being sent to public third-party endpoints.
Sentinel AI Agent Fleet
Sentinel Automations
2025

Sentinel required an enterprise-ready multi-agent automation platform capable of ingesting raw unstructured telemetry, diagnosing root causes, and executing safe remedial actions in real time without human fatigue.

THE ARCHITECTURE CHALLENGE

Eliminating hallucinations in mission-critical operations

Standard LLMs produce hallucinations and lack strict deterministic guardrails. Sentinel needed an autonomous system that could be trusted to make operational API calls, parse complex log formats, and adhere strictly to enterprise compliance guidelines.

01

High hallucination risk when handling edge-case system logs and customer queries.

02

Latency constraints requiring response generation under 150 milliseconds.

03

Data privacy mandates prohibiting sensitive customer PII from being sent to public third-party endpoints.

04

Complex orchestration needed between multiple specialized domain sub-agents.

Sentinel AI Agent Fleet technical architecture
SYSTEM TELEMETRY & AUDIT VERIFICATION
ENGINEERING METHODOLOGY

Deterministic multi-agent hierarchy with local privacy guardrails

HashKoda designed a LangGraph-powered hierarchical agent fleet with dedicated validation supervisor nodes, local vector memory indexing, and strict regex-level safety classifiers.

Deployed self-hosted open-weights models with custom LoRA fine-tuning on proprietary enterprise logs.
Engineered semantic caching layer in Redis, slashing repetitive inference costs by 64%.
Implemented real-time PII redaction and deterministic tool invocation validators.
Built comprehensive observability dashboards tracking token usage, latency, and reasoning traces.
VERIFIED RESULTS

82% faster triage and 1,400+ hours saved per month

1,400 hrs
Time Saved / Mo
118ms
Avg Latency
99.4%
Accuracy Rate
48
Active Sub-Agents
Reduced Mean Time To Resolution (MTTR) from 42 minutes down to under 7 minutes.
Saved an estimated $420,000 annually in operational overhead and manual support headcount.
Zero data leaks or compliance violations recorded across all tenant partitions.
Seamless scale handling 10x traffic bursts during Black Friday without degradation.
SYSTEM ARCHITECTURE STACK

Core Technologies & Frameworks

Python
FastAPI
LangGraph
PyTorch
PostgreSQL / pgvector
Redis
Docker
Next.js
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