Agentic AI Solutions
Applied AI-driven automation to enhance operational efficiency, accelerate decisions, and unlock new revenue.
Overview
Organizations struggle to scale knowledge work and decision-making. I design agentic systems that plan, act, and learn with strong guardrails—reducing manual toil and turnaround time without compromising accuracy.
Problems We Solved
Repetitive, high-variance tasks with long context windows (support, research, ops)
Fragmented tools and data sources across SaaS, APIs, and internal systems
Inconsistent decisions due to missing SOPs and limited observability
Human-in-the-loop needed, but not instrumented for quality feedback
Approach
- Task decomposition and policy design
- Break objectives into steps; define policies, tool permissions, and safety bounds.
- Tool orchestration
- Function calling to CRM, billing, data warehouses, and internal APIs.
- Retrieval + memory
- Vector stores for SOPs and cases; short- and long-term memory for continuity.
- Human-in-the-loop (HITL)
- Escalation gates; reviewers approve or correct; feedback loops adjust behavior.
- Observability
- Traces of actions/tools, tokens, and success criteria; drift detection and rollbacks.
Reference Architecture
- Orchestrator (Node/TypeScript) with tool router and policy engine
- LLM provider(s) with function calling and JSON-mode guarantees
- Vector store for SOPs/FAQs/cases; relational DB for states and audit log
- Event bus for workflows; queue workers for retries and backoffs
Outcomes
- 40–65% reduction in time-to-resolution on targeted workflows
- 25–45% fewer handoffs to senior staff due to better triage and SOP adherence
- Net-new capability: 24/7 coverage for backoffice tasks at consistent quality
Key Metrics
| Metric | Before | After | Delta |
|---|---|---|---|
| TTR (support triage) | 18m | 8m | −56% |
| Manual touches per ticket | 2.3 | 1.1 | −52% |
| First‑pass accuracy | 78% | 91% | +13pp |
Implementation Roadmap
- 2–3 day discovery: SOPs, data sources, risk review
- 2–3 weeks pilot: one high-ROI workflow with HITL
- 3–6 weeks scale-out: add tools, policies, and monitoring
Stack
- Node/TypeScript orchestrator, Next.js surfaces
- LLMs with function calling; embeddings for RAG
- Vector DB (e.g., Pinecone/pgvector), Redis, Postgres
- OpenTelemetry + structured logs
Looking to ship an agent that actually works in production? Let’s start with a pilot on your highest‑ROI workflow.
