Private implementation

AI Business Agent Platform

A configurable, multi-tenant AI platform for business conversations, knowledge retrieval, customer consultation, and assisted workflows.

Technical architecture Conceptual flow

Customer request
Agent orchestrator
Knowledge & memory
Tools & LLM gateway
Response validation
Persistence & observability

Project overview

A configurable, multi-tenant AI platform for business conversations, knowledge retrieval, customer consultation, and assisted workflows.

My role

Full-Stack & AI Backend Engineer

The problem

Business conversations require more than a model call: tenant boundaries, relevant knowledge, controlled tool access, consistent configuration, and a way to evaluate changes before release.

What I delivered

Designed the modular agent backend and orchestration architecture with tenant-scoped configuration, permissions, and persistence.
Integrated multiple LLM providers with routing, timeouts, failover, and provider-health visibility.
Built knowledge ingestion, chunking, hybrid lexical retrieval, vector-related infrastructure, and source citation workflows.
Implemented persistent conversations, memory, customer context, business-intent identification, lead qualification, and human escalation.
Developed configurable tools, business integrations, response guardrails, and validation workflows.
Built versioned configuration, release controls, evaluation scenarios, scoring, release thresholds, and rollback mechanisms.
Implemented usage monitoring, latency tracking, cost estimation, and a custom Next.js administration panel for agents, knowledge, conversations, evaluations, and releases.

Architectural focus

The diagram above shows the conceptual workflow. The orchestrator coordinates context retrieval, controlled tool execution, provider access, response validation, and persistence. Configuration, evaluation, and release controls support changes to that workflow.

Engineering challenges

Coordinating provider failures, retrieval, tools, and persistent context without losing the tenant boundary.
Making agent configuration changes reviewable through evaluations and release control.

Technical decisions

Keep provider access behind a gateway so routing, timeouts, and failover have one responsibility.
Separate configuration versions from active releases and use evaluation thresholds and rollback controls.

Implementation context

Implemented functionality is described from the supplied project brief. Deployment scale and activated infrastructure are not independently verified. Locally hashed retrieval vectors are not semantic embedding-model outputs. No foundation-model fine-tuning or incremental token-streaming claim is made. No authorized administration screenshot was supplied.

Private client work

No verified public destination or authorized application screenshot is included. The technical description is shared without confidential source code, customer data, or credentials.