AI agents that do the work, with you in control
Gjethe builds AI agents that complete multi-step tasks inside your tools: researching leads and updating the CRM, triaging support tickets, drafting and filing documents, scheduling and following up. Each agent gets only the permissions it needs, asks for human approval before risky actions and logs every step it takes.
- Free first consultation
- Written quote for your project
- Flexible payment milestones
At a glance
- Acts in
- CRM, email, calendar, docs, APIs
- Control
- Scoped permissions, human approval
- Transparency
- Every step logged
- Built with
- Tool calling and MCP
Definition
What is AI agent development?
An AI agent is software that uses a language model to plan and carry out a sequence of steps towards a goal, calling tools (APIs, databases, search, email) and checking results along the way, rather than only producing text.
A chatbot answers; an agent acts. That makes agents powerful and also raises the stakes, because a wrong action costs more than a wrong sentence. We design agents like any other system that touches business data: narrow scope, minimal permissions, clear checkpoints where a person approves, and logs that show exactly what happened and why. Many tasks turn out to need a reliable workflow with one AI step rather than a fully autonomous agent, and we'll say so.
Who is it for?
Sales and business development
Researching prospects, enriching records, drafting personalised outreach and keeping the CRM current.
Support teams
Triaging incoming tickets, gathering account details, drafting replies and routing complex cases.
Operations and back office
Processing documents, reconciling records, chasing missing information and updating systems.
Software products
Adding agentic features that let your users delegate tasks inside your product.
What's included, and what you keep
What's included
- Task analysis: what the agent should and shouldn't do
- Tool design: the actions the agent can take
- Permission model with least privilege
- Human-in-the-loop approval steps
- Planning and reasoning prompts
- Integration with CRM, email, calendar and internal APIs
- MCP servers for reusable tool access
- Guardrails, limits and fallbacks
- Step-by-step audit logs
- Evaluation on realistic test tasks
- Cost controls and usage monitoring
- Operator dashboard to review and correct runs
What you receive
Handed over at the end. Yours to keep.
- Deployed agent on your infrastructure
- Tool and permission documentation
- Evaluation results on test tasks
- Monitoring and audit log dashboard
- Runbook for operators
How does an AI agent work?
Goal
A request in plain language, from a user or a schedule
Plan
The model breaks the goal into steps
Use tools
Calls your CRM, calendar, database or APIs: only the ones it is allowed
Check
Validates results; risky actions wait for human approval
Done & logged
Result returned, every step recorded
Workflow, assistant or agent?
Not every task needs full autonomy. Choosing the lightest option that works keeps things reliable and affordable.
| Approach | What it does | Choose it when |
|---|---|---|
| Automated workflow | Fixed steps with one AI step, such as classifying or extracting | The process is predictable |
| Assistant (copilot) | Suggests drafts and actions; a person decides | Judgement matters and volume is moderate |
| Agent | Plans and executes steps itself, with approval points | Tasks vary and need several tools to complete |
Safety rules every agent we build follows
- Only the tools and data the task requires
- Read access by default; write access only where needed
- Human approval for sending, paying, deleting or publishing
- Spending and step limits per run
- Every tool call and decision logged
- Easy switch to pause the agent
- Clear failure messages instead of silent guesses
Example agents we build
Lead research agent
Researches new sign-ups or inbound leads, enriches the CRM record and drafts a personalised first email for approval.
Ticket triage agent
Reads incoming tickets, gathers account context, sets priority and drafts a reply or routes to the right team.
Document processing agent
Checks submitted documents for completeness, extracts data and requests missing items by email.
Scheduling agent
Coordinates meetings across calendars, proposes times and follows up until a slot is confirmed.
How AI agent development works, step by step
Every step has a clear output you can review. Timing is agreed in your quote.
- 1
Pick the task
A specific, repetitive task with clear success criteria: not “automate the department”.
- 2
Map tools and permissions
The actions the agent needs, at the lowest permission level that works.
- 3
Prototype
A first version tested on real examples to see where it succeeds and fails.
- 4
Add guardrails
Approval steps, limits, validation and fallbacks around the weak spots.
- 5
Evaluate
A test set of tasks measured for success rate, errors and cost.
- 6
Deploy and supervise
Launch with operators reviewing runs, then widen autonomy as trust is earned.
Tools, and what shapes your quote
Pricing is based on your project and agreed in your quote. These are the things that move it.
| Factor | Why it matters |
|---|---|
| Number of tools | Each system the agent can act in needs a well-designed, tested tool. |
| Task variability | Predictable tasks are cheaper to make reliable than open-ended ones. |
| Risk of actions | Actions involving money, customers or deletion need more guardrails and approval flows. |
| Evaluation depth | Building a strong test set takes time but is what makes the agent trustworthy. |
| Run volume | Model usage costs scale with how many tasks the agent performs. |
| Area | Tools |
|---|---|
| Models | Anthropic Claude, OpenAI, Google Gemini |
| Agent tooling | tool calling, Model Context Protocol (MCP), Vercel AI SDK, LangGraph |
| Integrations | HubSpot, Salesforce, Gmail and Outlook, Google Calendar, Notion, internal APIs |
| Observability | Langfuse, structured audit logs, cost dashboards |
Common mistakes to avoid
What we see go wrong most often, and how a good project avoids it.
Too much autonomy too soon
Start with approval on every important action; remove checkpoints only after the agent proves itself.
Broad permissions
An agent with admin access to everything is a security incident waiting to happen.
No evaluation set
Without test tasks, you can't tell whether a prompt change made the agent better or worse.
Agents for predictable work
If the steps never change, a workflow with one AI step is cheaper and more reliable.
Invisible reasoning
When something goes wrong, logs of each step are the only way to understand why.
Frequently asked questions
What's the difference between a chatbot and an AI agent?
A chatbot responds to messages. An agent works towards a goal by planning steps and using tools (searching, updating records, sending drafts) with checkpoints where a person approves.
Is it safe to let AI take actions in our systems?
With the right design, yes: minimal permissions, human approval for consequential actions, limits per run and complete logs. We start conservative and widen autonomy only when results justify it.
What is MCP?
The Model Context Protocol is an open standard for connecting AI models to tools and data. Building tools as MCP servers makes them reusable across different AI assistants and agents.
Which tasks are good candidates for agents?
Repetitive tasks that need several steps across tools and some judgement, lead research, ticket triage, document processing, scheduling and follow-ups.
How reliable are agents?
Reliability depends on the task and the guardrails. We measure success rates on a test set before launch and share the results, so you decide how much to automate.
What does an agent cost to run?
Model usage per task plus hosting. Multi-step tasks use more tokens than single answers; we estimate costs per task and set limits.
Can the agent work inside our existing chat tools?
Yes, agents can be triggered and supervised from Slack, Teams, email or a simple web dashboard.
What happens when the agent gets stuck?
It stops, explains what it tried and why it couldn't continue, and hands the task to a person rather than guessing.
Can we see what the agent did?
Yes. Every run is logged with the plan, each tool call, results and approvals, viewable in an operator dashboard.
Do we own the agent?
Yes. Code, prompts, tools and logs run on your accounts and belong to you.
Can an agent work across several of our tools at once?
Yes, that's where agents add most value. A single run might read an email, look up the customer in the CRM, check an order in the store and draft a reply, each through its own scoped tool.
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AI Agent Development by industry
Put an AI agent to work.
Tell us what you need. You get an honest assessment, a clear plan and a written quote. The first consultation is always free.
- What happens next
- 1You tell us what you need
- 2We aim to reply within 24 hours with questions
- 3You get a written quote for your project