AI agents
AI agent development for production systems
An agent is a loop that calls tools with real credentials. Most agent projects fail on the second half of that sentence. The model picks the wrong tool, retries a payment, loops on a malformed response, or takes an action nobody can trace a week later.
- Tool and MCP layer
- Permission scopes
- Human approval gates
- Idempotent execution
Start a project
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Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.
Signs you need this
We build agents that are boring to operate. Narrow tool surfaces, typed inputs and outputs, permission scopes per action, a human approval step on anything irreversible, and a log that shows every model call, tool call and decision in order.
- An agent demo that works on three examples and fails on the fourth
- Nobody can say which actions the agent is allowed to take
- Retries that duplicate side effects like emails or refunds
- No record of why the agent did what it did
- Token spend per task climbing with no ceiling
- Internal tools with APIs but no MCP or tool layer
What you get
What is included
Tool and MCP layer
Typed tool definitions and MCP servers over your APIs, databases and internal services, with schemas the model can't misread.
Permission scopes
Each tool carries its own scope and credential. Read paths and write paths are separated, and write paths are rate limited.
Human approval gates
Irreversible or high-value actions pause for a named approver in Slack, email or your own UI, then resume with the decision logged.
Idempotent execution
Idempotency keys, checkpointed state and safe retries, so a timeout halfway through a workflow doesn't send the invoice twice.
Audit trail
Every step stored: prompt, model, tool call, arguments, result, latency and cost. Replayable for debugging and review.
Agent evals
Task-level success rates on recorded scenarios, run on every prompt or model change before it reaches users.
How it runs
How we deliver AI agents
- 01
Map the workflow
Write down the job the agent does, the systems it touches, and which steps a human must approve.
- 02
Build the tool layer
Typed tools or MCP servers over the real systems, tested without a model in the loop first.
- 03
Agent loop and gates
Orchestration, approval steps, budgets per task and the audit log, deployed against a scenario suite.
- 04
Supervised rollout
Shadow mode, then approval-required mode, then autonomy on the actions that earn it by measured success rate.
Industries we build this for
AI agents questions
Something else? Email t@moher.ai and an engineer will answer it.
How much does it cost to build an AI agent?
A single-workflow agent with a handful of tools and approval gates typically runs $40,000 to $120,000. Multi-system agents with custom MCP servers, complex permissions and a full eval suite run higher. We scope it in writing in the first week.
Which models and frameworks do you use?
Claude, GPT and Gemini for most agent work, open-weight models where data residency or cost requires it. We keep orchestration thin and in your codebase rather than locking you into a heavy framework.
What is an MCP server and do we need one?
Model Context Protocol is a standard way to expose your tools and data to models. If more than one agent or client will use the same internal systems, an MCP server saves rebuilding the tool layer each time.
How do you stop an agent from doing something wrong?
Scoped credentials per tool, hard budgets per task, validation on every tool argument, and human approval on anything irreversible. The agent can only take actions the system already allows.
Can the agent run inside our infrastructure?
Yes. We deploy into your cloud account, behind your auth, with your secrets manager. Nothing routes through a platform we control.
How long until an agent is in production?
A first supervised workflow usually ships in four to six weeks. Autonomy expands after that as success rates hold on real traffic.
Tell us what you need built
Four quick questions and an email. An engineer reads every brief and replies within one business day with next steps, and a written scope follows within a week.
- Written scope and price within a week
- Production deploy by week three
- Your repo and cloud from the first commit
- Senior engineers only, no account managers
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.