Custom AI Agent Development for Engineering Teams
Agents, harnesses, MCP tool servers and multi-agent systems, built into your product by someone who ships with agents every day.
Custom AI agent development means designing and shipping agents that own real jobs inside your product: the harness around the model, the MCP tool servers it uses, the evals that prove it works, and multi-agent orchestration only when one agent is the bottleneck. Lance Ennen builds these for startups and engineering teams, drawing on an AI-native practice where coding agents ship alongside humans daily, with deep experience in digital identity and payments systems.
By Lance Ennen, Founder & Fractional CTO · Published September 12, 2026 · Last reviewed September 12, 2026
What You Get
Agent harness design and build
An agent is a model plus a harness. I build the harness: the agent loop, tool dispatch, memory and state, sandboxing, permissions, context management and cost limits. Every component encodes an assumption about what the model cannot do on its own, so it is designed, not defaulted.
Custom agents for your product
Customer-facing and internal agents that own one job end to end, on Claude, OpenAI or open-weight models, deployed on your infrastructure with your data staying where it is.
MCP tool servers and integrations
Your APIs, databases, CRM and internal systems exposed to agents through MCP servers, with authentication, scoped permissions and audit logs, so the model can change without the integrations changing.
Multi-agent orchestration
Planner, worker and evaluator patterns; sub-agents and handoffs; approval gates and human-in-the-loop steps where the blast radius demands them. Added only when a single agent is measurably the bottleneck.
Evals, guardrails and observability
Offline eval sets and evals in CI, red-teaming of tool use, tracing, and cost monitoring, so you know an agent works before it ships and keep knowing after.
Agent teams for your engineering org
Claude Code or OpenClaw rollout for your engineers, a merge policy that scales review to risk, and the operating prompts that keep coding agents productive inside the lines. This is how my own practice runs.
The harness framing follows Anthropic's engineering notes on effective harnesses for long-running agents and harness design. I use OpenClaw and Claude Code heavily; I did not build them. See OpenClaw deployment for the self-hosted agent layer.
Who This Is For
- Product teams with a real workflow an agent should own, not a chatbot to demo
- Startups building AI-agent, identity or payments products that need the harness done right the first time
- Engineering orgs adopting Claude Code or OpenClaw and wanting a merge policy, not a mess
- Teams that already tried a framework demo and hit the wall in production
Who This Is NOT For
- A chatbot for the marketing site: a no-code tool will do that for less
- Training a model from scratch: this is harness and integration work, not ML research
- Teams that want a multi-agent system before a single agent has passed an eval
Not sure whether you need an agent at all? Start with the architecture ladder
Built for My Own Products First
Every pattern on this page runs in something I ship. The evidence links to it.
Pairty: an AI-native engineering team
Pairty is built by coding agents working alongside humans, in a Turborepo of a NestJS API, Next.js apps, contracts and a React Native mobile app. The operating prompts that run that team are published.
Read the operating prompts →CredZK: a working prototype in five days
zkTLS financial credentials built at Moca Network's Identity House in Cannes, second place on Demo Day, now part of Permara. The build speed came from the harness, not the headcount.
CredZK case study →The agent architecture ladder
Start as a product feature, add orchestration only under customer load. The framework I use to decide when an agent should become a system.
How to architect AI agent systems →A merge policy for agent-written code
Three tiers sorted by blast radius: agent-autonomous, agent drafts and human reviews, human-only. The policy my teams run, with the industry data behind it.
Should AI agents write production code? →How Engagements Work
- 1
Discovery sprint
One to two weeks. Map the workflow, pick the first agent worth building, define what "working" means as an eval set, and decide how much harness you already have.
- 2
Architecture blueprint
A written design: model choice, tool boundaries and permissions, memory and state, orchestration (if any), approval gates, observability and the cost envelope.
- 3
Build and harden
The agent, its MCP servers and its evals, shipped into your codebase and your infrastructure with tracing on from day one.
- 4
Handoff or retainer
Your engineers own it, with documentation and training, or I stay on a retainer to extend it. Scoped per engagement; it starts with a call.
Need ongoing leadership rather than a build? That is the fractional CTO engagement.
Custom AI Agent Development FAQ
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Have a Workflow an Agent Should Own?
Start with a discovery call. If an agent is the wrong tool for the job, I'll say so and point you at what is.
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