Autonomous AI Agent Systems & LLM Orchestration
Engineered for organizations seeking autonomous operational workflows, intelligent customer concierge pipelines, and multi-step cognitive automation.
Production System Blueprint
Modular, horizontally scalable infrastructure engineered for resilience, high throughput, and zero single points of failure.
Frontend & Presentation
Real-time streaming agent chat interface, human-in-the-loop approval console
Backend & API Services
LangGraph / Python agent runtime, FastAPI async orchestration, Langfuse observability
Database & State Layer
Qdrant / pgvector vector store, PostgreSQL for conversation state checkpoints
Cloud & DevOps Pipeline
OpenAI / Anthropic Claude API gateways, AWS Bedrock, Redis pub/sub streaming
Live Production Benchmarks
Every system built by MIHA Technologies is instrumented with rigorous automated latency, throughput, and reliability SLAs.
Server-Sent Events (SSE) streaming direct to browser interface.
HNSW index vector lookup across 500k embedded documents.
Strict Pydantic JSON schema constraints with auto-correction fallback loops.
Recursive state summarization preventing LLM context window bloat.
Architectural Trade-Off Matrix
Deliberate technology selections evaluated on operational complexity, infrastructure cost, and developer velocity.
Enables cyclical reasoning, dynamic branching, error recovery loops, and explicit human-in-the-loop interruption gates.
Boosts document retrieval precision from 71% to 94%, eliminating hallucinations caused by semantic drift.
Allows long-running workflows to pause, survive server reboots, and resume exactly where left off.
Sprint Delivery Roadmap
Fixed-sprint engineering milestone cadence designed for full visibility and rapid production deployment.
Phase 1: Knowledge Ingestion & Vector Indexing
- Document parsing pipeline (PDF, Markdown, Notion, Database)
- Chunking strategy optimization with semantic boundaries
- Qdrant / pgvector collection creation and benchmark evaluation
Phase 2: LangGraph Agent Graph Design
- State schema and node graph definition with conditional routing
- Tool call definitions with strict Pydantic validation
- Human-in-the-loop review and approval breakpoints
Phase 3: Real-Time Streaming UI & Memory
- FastAPI SSE streaming endpoint and React chat canvas
- Conversation memory thread management and token pruning
- Langfuse observability integration for latency and cost tracking
Phase 4: Evaluation, Hardening & Launch
- Automated regression test suite evaluating agent task completion
- Prompt injection red-teaming and safety guardrails
- Production rollout with fallback model routing (Claude 3.5 & GPT-4o)
Security & Compliance Standards
Military-grade protection built into the application data flow from day one.
Frequently Asked Questions
Deep technical answers regarding integration, scale, and operational handover.
How do you prevent AI agents from hallucinating in production?
We enforce strict grounding constraints using hybrid RAG (dense vector + sparse BM25) and re-ranking. The agent is instructed to cite explicit source metadata and reject queries outside its verified context window.
Can the agent interact with our existing internal APIs and databases?
Yes. We build custom agent tools with typed schemas that query your internal REST/GraphQL endpoints, database views, or CRM systems with fine-grained access tokens.
What happens if an LLM provider experiences an outage?
Our orchestration layer includes automated fallback routing. If your primary model encounters rate limits or downtime, traffic fails over seamlessly to alternative enterprise providers.
Ready to engineer your production system?
Speak directly with our senior infrastructure architects to review your technical specs, stack requirements, and sprint timeline.
Schedule Architecture Consultation →