AI agent development for production traffic
If you need more than a single long system prompt in ChatGPT, you need orchestration: explicit phases, retrieval, evaluation hooks, and cost-aware model routing. That is what I build.
Most teams that reach out have already tried a "one bot" approach and hit one of three walls: runaway API cost, untrusted answers without grounding, or no path to a qualified lead / ticket in their CRM. My work packages those concerns into a system your operators can run—not a demo you can't maintain.
What you get
- Graph-style orchestration (e.g. LangGraph): greet, discover, handle objections, portfolio proof, capture, guardrails—testable as separate paths.
- RAG over your truth: FAQs, spec sheets, case studies, pricing rules—chunked and versioned so marketing updates do not require redeploying prompts blindly.
- Tiered LLM routing: rules → efficient models → escalation only when needed; logs per tier so finance can reconcile spend with outcomes.
- Structured outputs: HOT/WARM/COLD (or your taxonomy), CRM- ready payloads, email handoffs—whatever your sales or support motion needs.
- Operator surfaces: prompt/knowledge admin, conversation replay, cost dashboards—exact scope depends on your stage, but something always exists for non-dev iteration.
How engagements usually run
- Discovery call — outcomes, constraints, success metrics, and compliance boundaries (PII, regions, SLAs).
- Written scope — milestones, acceptance tests, and a realistic cut-line for v1 vs later phases.
- Build in slices — each slice is demoable: e.g. RAG + one happy path, then objection handling, then routing, then dashboards.
- Production hardening — rate limits, logging, rollout plan, runbooks; optionally handoff to your team with pairing.
Proof
The public reference build is Ramy—an eight-agent LangGraph system with strong cost reduction versus a naive single-model chat, live in production. Read the case study, the deep-dive architecture article, and try the demo. For budget framing, see 2026 pricing notes and pricing ranges.
FAQ
- Do you only build sales agents?
- No. The same patterns—stateful graphs, RAG, routing, guardrails—apply to support, internal ops, and onboarding flows. Sales is just a common high-ROI first use case.
- What stack do you prefer?
- Python/FastAPI for agent APIs is common on my builds; LangGraph or equivalent for orchestration; vector DB (often Qdrant) for retrieval; PostgreSQL for durable state; React for operator dashboards when needed.
- How do you control LLM cost?
- Tiered routing: rules and smaller models for volume; stronger models only when intent or revenue risk warrants it. Summarization and caching where safe. Every tier is logged so we can prove savings—see the Ramy case study.
- How long is a typical first production slice?
- Depends on scope: a focused MVP (one primary flow + RAG + basic ops) is often weeks, not days. Larger multi-dashboard builds run on milestones with explicit acceptance tests.
- Can you join an existing team?
- Yes. I can lead the workstream, pair with your engineers, or own the backend/agent layer while your team owns frontend or product.