Build with Claude — co-workers, subagents, skills, and custom MCP.

Claude, in production

We build deeply on Anthropic’s Claude because it is a strong foundation for agentic work — long-horizon reasoning, reliable tool use, and an open integration standard in MCP. humaineeti remains model-agnostic (BYOM — bring your own model), but our default agentic stack is Claude, the Claude Agent SDK, and the Model Context Protocol.

• Agents that finish the work. Not a chatbot bolted onto a workflow — agents that research, build, evaluate, deploy, and govern, with humans in the loop where it counts.
• Adaptive thinking & effort control. Claude decides how much to reason per step; we tune the effort/cost/quality tradeoff per route rather than paying for maximum reasoning everywhere.
• Prompt caching for efficiency. Stable context is cached across turns, so long-running agents pay for what changes, not for re-reading the same preamble every request.

AI co-workers & subagents

The unit we design for is the Claude co-work — a team of Claude-powered agents that a single human directs. Each agent is accountable for its slice of the job, and human-in-the-loop guardrails sit at the decisions that matter (approvals, destructive actions, external side effects).

Subagents are how that team stays focused. A lead agent delegates independent sub-tasks to separate agents, each running in its own context window with its own tools — and only their conclusions return to the lead, so the main loop never drowns in detail it will not reuse. It is a first-class pattern across Claude’s agent tooling:

• In Claude Code, subagents run as distinct agent types — a read-only Explore agent that fans out searches, or custom agents you define under .claude/agents/ — spawned in parallel, each isolated from the others’ context.
• On the Claude Agent SDK and Managed Agents, a coordinator agent delegates to a roster of agents, each running in its own context-isolated thread. Delegation is one level deep, so workers stay predictable and do not recursively spawn.

Two things make this pay off. Subagents can run on cheaper, faster Claude models — Haiku 4.5 is an excellent subagent model — so delegation also controls cost. And a fresh-context verifier subagent that reviews the lead agent’s work tends to catch more than self-review, because it judges the output without the bias of having produced it. The rule of thumb we build to: use subagents for parallel or independent workstreams, and keep tightly sequential steps in the main loop.

Agent Skills

A general-purpose agent that has to carry every specialist instruction in its context all the time is slow, expensive, and easy to confuse. Agent Skills solve that. A skill is a reusable, filesystem-based capability package — a folder with a SKILL.md — that loads on demand through progressive disclosure.

• The skill’s short description sits in context by default, so the agent knows the capability exists.
• The full instructions load only when a task actually calls for them — no context bloat, no cost paid for guidance that isn’t needed.
• The effect is a general Claude agent that becomes a specialist on your workflow the moment the work demands it, then steps back down.

humaineeti packages your recurring, well-specified workflows as Skills so they are consistent, versioned, and reusable across every agent that speaks Claude.

Custom MCP development

The Model Context Protocol (MCP) is the open standard, introduced by Anthropic, for connecting agents to tools, data, and services. An MCP server exposes its capabilities through one discoverable, model-agnostic interface — the same server works with Claude or any other MCP-aware client. Use established servers for commodity integrations; build your own for the systems that encode your business. That second half is custom MCP development, and it is where we do our deepest work.

Every custom server humaineeti builds ships with scoped credentials per consumer, allow-lists on which tools an agent may call, and audit logging on every invocation — so an agent gets exactly the access the task needs, and every action is traceable.

How we build (Agent SDK + tool use)

Under the hood, our agents are built on the Claude Agent SDK and Claude’s tool use. You define your own tools; the SDK’s tool runner drives the agentic loop — calling the model, executing the tool, feeding results back, and repeating until the work is done.

• Tool runner for autonomy. For flows we want to run end-to-end, the tool runner handles the loop automatically.
• Manual loop for control. Where a step needs an approval gate or human-in-the-loop confirmation, we write the loop by hand and stop for sign-off before a hard-to-reverse action.
• Effort & adaptive thinking. We tune reasoning depth per route to balance cost and quality — higher effort where correctness matters, lower where speed does.
• Prompt caching. A frozen system prompt and deterministic tool set keep the cache warm, so long agent runs stay efficient.

Governance & evaluation

Agentic autonomy without governance is a liability, so we build the controls in from the start. Under humaineeti’s Responsible AI and Zero-Trust posture, every agent loop and every LLM invocation is traced, logged, and scored — on correctness, completeness, safety, and tool-call effectiveness.

That evidence is produced and evaluated by AI Eval Service — our Eval@Core capability — so an agent’s quality is a measured number, not a hope. It pairs naturally with our Agent Evaluations practice and the GenAI Delivery Factory for production rollout.

The Claude model line

humaineeti is model-agnostic, but when the answer is Claude, we pick the tier that fits the step — and one agent loop often uses several at once.

On-page FAQ

Related reading

• Model Context Protocol (MCP) for Enterprise: 2026 Guide
• Agent Skills vs Frontier LLMs
• AI Eval Service — Eval@Core
• Agent Evaluations
• GenAI Delivery Factory
• Future of Work