AI Agent Development
Autonomous AI agents that plan, act, and complete tasks end-to-end.
LLM-powered agents that reason over tools, call APIs, and finish multi-step tasks without hand-holding — for support, ops, research, and content.
Problems I solve
- Repetitive knowledge work that needs judgment
- Support tickets requiring multi-system lookups
- Research and summarization at scale
- Content operations that need context awareness
What you get
- Tool-using agents (OpenAI function calling, Claude tools)
- RAG pipelines over your knowledge base
- Multi-agent orchestration for complex tasks
- Guardrails, evals, and cost controls
Use cases
Tier-1 support agent
Answers 60–80% of tickets from docs + tickets history, escalates the rest with context.
Research analyst agent
Given a topic, browses sources, extracts key facts, and delivers a structured brief.
Sales research agent
Enriches accounts with news, funding, tech stack, and personalized outreach hooks.
Internal ops copilot
Chat over your knowledge base + tools (Notion, Linear, HubSpot, Postgres).
Examples I've shipped
Docs-grounded support agent
Outcome — Auto-resolves ~65% of tickets with cited answers.
Multi-agent research crew
Outcome — Produces analyst-grade briefs in <5 min.
Case study
Challenge
Support team of 4 drowning in repetitive Tier-1 tickets, response SLA slipping past 8 hours.
Approach
- Ingested 1,200 help articles + 18 months of resolved tickets into pgvector
- Claude agent with citation-required tool schema
- Confidence-gated auto-reply, human review for low-confidence
- Weekly eval suite on 200 golden tickets
Results
How I work
- 1
Use case scoping
Define the agent's job, tools, and success metrics.
- 2
Architecture
Choose model, tool schema, memory strategy, and guardrails.
- 3
Build
Implement tool calls, RAG, and orchestration.
- 4
Evals
Golden dataset + automated evals before every deploy.
- 5
Deploy
Ship behind observability, cost caps, and human-in-the-loop.
Deliverables
- Production agent with tool integrations
- Eval suite and prompt versioning
- Cost and latency dashboards
- Documentation and handoff
Benefits
- Handle work that rule-based automation can't
- Scale expert judgment across your team
- 24/7 responsiveness without headcount
Frequently asked questions
Which model do you use?+
It depends on the task — GPT-class models for reasoning, Claude for long context, smaller models for classification. I benchmark before locking in.
How do you prevent hallucinations?+
RAG grounding, strict tool schemas, response validation, and evals on a golden dataset before every prompt change.