Claude AI Agents,
Built for Real
Business Outcomes
I design, ship, and operate production-grade Claude agents that close tickets, qualify leads, review code, and run workflows. MCP-native. Long-context. Eval-driven. Safety reviewed.
Six agent shapes I ship in production
Each one comes with eval suites, prompt-injection guards, observability, and a runbook your on-call engineer can actually use.
Customer Support Agents
Resolve 40-70% of inbound tickets without escalation. Reads your knowledge base, handles refunds via tool use, escalates with full context.
Sales & Outreach Agents
Researches accounts, writes personalized outbound, qualifies inbound, books meetings. Connects to your CRM via MCP, never invents data.
RAG Pipelines
Hybrid search over your docs, tickets, and Notion. Reranking, citation tracking, freshness scoring. Long-context Claude does the heavy synthesis.
Code Review & Coding Agents
PR review with project-specific style rules. Spec-driven implementation. Test generation. Migration scripts. The agent reads your codebase before opening its mouth.
Workflow & Ops Agents
Multi-step workflows that span tools: read invoice from email, validate against PO, post to ERP, notify Slack, log audit trail. Fail-loud, retry-safe.
Voice Agents
Inbound and outbound voice with sub-second response. Claude reasons, ElevenLabs speaks, your CRM logs. Useful for triage, scheduling, FAQ.
Why I bet on Claude for production agents
Six honest reasons most engineers find out by themselves after 6 months of fighting another model.
Tool use that actually obeys
Claude follows tool schemas faithfully — when the agent has to choose between calling your API and inventing an answer, it calls the API. That is the gap between a demo and something you can safely put in front of customers.
200K context, used well
Long context only matters if recall stays sharp. Claude reads 50-page contracts, full codebases, and entire ticket histories without losing the thread. Fewer chunks, fewer retrieval misses, simpler RAG.
MCP-native integrations
Model Context Protocol gives the agent typed access to your tools without bespoke glue. Build one MCP server, plug into Claude Desktop, Claude Code, your custom app. Standards beat sprawl.
Safety reviewed, not bolted on
Constitutional AI training and Anthropic red-teaming mean fewer surprise behaviors in production. I add prompt-injection guards, PII redaction, and audit logs on top — so legal can sign off without a fight.
Extended thinking for hard agent loops
When the agent has to plan across many tool calls, Claude's extended thinking mode reasons before acting. Fewer wrong turns mid-loop, better recovery from errors, cleaner agent traces in production.
Production economics that scale
Sonnet handles the bulk at low cost, Opus tackles the hard 5%. Prompt caching cuts repeated-context costs by up to 90%. Batch API for offline jobs at half price. The unit economics actually work.
Four phases. No surprises.
Every phase has a deliverable you can inspect. Every gate is a written go/no-go you control.
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DiscoverWeek 1
Write the agent's job description
I sit with your operators for two days and write down exactly what the agent is and is not allowed to do. Eval set drafted. Success metrics signed.
DeliverablesAgent spec doc Eval set v0 Risk register Architecture sketch -
DesignWeek 2-3
Prototype the smallest useful version
A working prototype your team can poke at. Tool schemas, prompt scaffolding, and the first eval run. We kill bad ideas here, cheaply.
DeliverablesClickable prototype Tool schemas Prompt v1 First eval scores -
BuildWeek 4-8
Production code, tested under load
MCP servers, RAG pipeline, observability, prompt-injection guards, fallbacks, retries, audit logs. Soaked under shadow traffic before any user sees it.
DeliverablesProduction code Eval suite Runbook Dashboards -
DeployWeek 9+
Ship behind a flag, ramp on metrics
Staged rollout: 1% → 10% → 50% → 100%. Eval scores, latency, cost-per-resolution watched live. Retainer kicks in for tuning, model upgrades, incident response.
DeliverablesFeature flag On-call runbook Cost dashboard Monthly reviews
Real Claude work you can click and verify
No stock logos or invented numbers. The first two are live on this site — open them and click around. The other two are public GitHub repositories you can read and run yourself.
Agent Skills Marketplace
50+ ready-to-use Claude agent skills
A live marketplace of Claude agent skills — each one a full, copy-pasteable prompt for development, DevOps, data analysis, security testing, and content work. Built, categorized, and maintained on this site.
- Claude skills
- 50+ Claude skills
- categories
- 6 categories
- on this site
- Live on this site
AI Prompt Library
850+ production prompts, many tuned for Claude
A curated prompt library with usage notes, categories, and one-click copy. Hundreds of production prompts — searchable, translated, and free to use. Live on this site, not a screenshot.
- prompts
- 850+ prompts
- languages
- 6 languages
- on this site
- Live on this site
nexa
Multi-agent AI platform, powered by Claude
An open, public codebase: a multi-agent platform built on Claude that coordinates specialized agents through a shared task pipeline. Read the source, fork it, and run it yourself.
- agent platform
- Multi agent platform
- powered
- Claude powered
- on GitHub
- Public on GitHub
my-ai-crew
An AI agent team, powered by Claude Code
A public repo where a crew of Claude Code agents split up and run an automated content workflow end to end. Open to read, fork, and adapt.
- content crew
- Agents content crew
- Code
- Claude Code
- on GitHub
- Public on GitHub
Recognized in the Claude ecosystem
Open-source contributions, partner program, and community work that pre-vets the engineer before you hire.
Anthropic Solutions Partner — applicant
Submitted application to the Anthropic Partner Network for solutions delivery. Working through the technical review track focused on agentic systems, MCP integration, and enterprise rollouts.
Visit Anthropic Partner NetworkLocal-first Claude integrator agent for business ops
Framework for safer bash execution in AI coding agents
Open source
Public repositories you can read, fork, and run — real Claude agent code, not screenshots.
Published in 6 languages
Every long-form article is translated across six locales — English, Dutch, German, Spanish, Portuguese, and French — so the work reaches a global audience.
Read the blog200+ technical articles
Long-form, hands-on writing on Claude, MCP, RAG, and agentic systems — the same references I use on client builds.
Read the blog1,500+ projects delivered
8+ years of freelance and contract work across web and AI builds. References available on request.
See case studiesThe questions smart buyers ask
If a question is missing here, ask it in the contact form. Honest answers, no vendor-speak.
How long does a Claude AI agent project take?
A focused MVP agent ships in 3-6 weeks. Multi-tool agents with RAG, evals, and production guardrails ship in 8-14 weeks. Voice and multi-agent orchestration take 12-20 weeks. I always start with a 1-week discovery to right-size the timeline before quoting.
What does a Claude agent project cost?
Pilots start at $5,000 USD. Production agents with RAG, MCP tooling, and observability range $15,000-$45,000. Custom enterprise builds with SLAs, SOC 2 work, and multi-agent systems run $45,000-$75,000+. Retainers from $2,500/month for ongoing tuning.
Do you build with MCP (Model Context Protocol)?
Yes. MCP is the default integration layer. I build custom MCP servers for your CRM, database, internal APIs, and SaaS tools so the agent connects safely without bespoke glue code. MCP servers also work in Claude Desktop and Claude Code, so your team gets value beyond the agent itself.
How do you handle data privacy and compliance?
Anthropic Claude does not train on your API data. I use Anthropic Workbench, AWS Bedrock, or Google Vertex AI depending on your compliance posture. PII redaction, prompt-injection guards, audit logs, and per-tenant key isolation are built in. SOC 2, HIPAA, and GDPR-ready architectures available.
Can Claude work with my existing stack?
Yes. I integrate with Laravel, Node, Python, Next.js, Postgres, MySQL, Pinecone, Weaviate, Redis, AWS, GCP, Azure, and any system with a documented API. MCP makes connecting to internal tools straightforward. If your stack is unusual, that is a conversation I am happy to have on a discovery call.
What is RAG and do I need it?
RAG (retrieval-augmented generation) gives the agent access to your private knowledge base — docs, tickets, product data, contracts. You need it whenever answers must reflect information the model has not seen during training. Most production agents need some form of RAG; I use hybrid search with reranking and citation tracking by default.
What happens if the agent gets something wrong in production?
It will. Every agent ships with: an eval suite that runs on every prompt change, a feature flag for instant rollback, structured logging so you can replay any conversation, escalation paths to a human, and a runbook your on-call engineer can follow at 3am. Failure modes are designed for, not hidden.
Do you offer ongoing support after launch?
Yes. Most clients move to a monthly retainer covering eval runs, prompt tuning, model upgrades when Anthropic ships new Claude versions, cost monitoring, and incident response. Retainers start at $2,500/month and scale with usage.
Let's ship the agent
that pays for itself
One discovery call, one written proposal in 48 hours, one go/no-go decision. No commitment until you sign.
