Custom AI Agents and Workflow Automation

Build practical AI systems for real business work.

BeLogical designs agentic workflows that connect language models, business data, approvals, and operational tools into reliable automation.

Core stack .NET 10, Semantic Kernel, Qdrant, n8n

Agent patterns RAG, tool use, MCP, human approval

Primary fit Fintech, operations, support, internal copilots

Workflow Blueprint

Map the process, data sources, risks, and integration points before writing production code.

Build one focused workflow with retrieval, model calls, approval gates, and measurable outcomes.

Deploy hardened agents with logging, evaluations, fallbacks, access control, and monitoring.

Agent Builder

Blueprint

Local planner

Intake agent: classify the request, extract structured fields, and detect priority.
Research agent: retrieve relevant context from documents, tickets, CRM records, and vector search.
Action agent: draft responses, create tasks, update systems, or prepare handoff packages.
Reviewer agent: check policy, data exposure, confidence, and required human approvals.

Capture the incoming work item and normalize it into a typed workflow state.
Retrieve context using hybrid search across source systems and Qdrant-backed knowledge.
Generate a suggested action plan with confidence, sources, and next-best action.
Route high-risk or low-confidence actions to a human reviewer.
Execute approved actions through APIs, n8n workflows, or MCP tools.
Log decisions, prompts, retrieved context, and final outcomes for audit and improvement.

Human approval before customer-facing replies or system updates
Keep audit logs, avoid exposing restricted data, and preserve human accountability.
Restrict tool access by agent role and workflow stage.
Capture citations and source metadata for every generated recommendation.
Block autonomous execution when confidence, authorization, or policy checks fail.

Discovery: map the process, data owners, exception paths, and success metrics.
Pilot: build one working workflow with test data, approval gates, and observability.
Production: connect live systems, harden security, add evaluations, and deploy monitoring.
Iteration: review outcomes, tune prompts/retrieval, and expand the agent toolset.