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Custom AI Agents
A chatbot answers questions. An AI agent does work. It can read an incoming request, look things up in your CRM or database, decide on the next step, call the right tools, and either complete the task or prepare it for a person to approve. Examples include triaging support tickets, preparing quotes from enquiry emails, reconciling order data, researching leads before a sales call, or chasing missing documents.
We build agents using models from OpenAI and Anthropic's Claude, with function calling so the agent can use specific, limited actions in your systems. Where it fits, we build them inside n8n so the agent sits within a visible workflow your team can follow. Agents can have memory of previous steps or customers, access to your documents through retrieval, and custom logic for the rules your business follows.
The difference between a useful agent and a risky one is control. We give each agent only the permissions it needs, add guardrails on what it can say and do, require human approval for anything that sends money, emails customers, or changes important records, and log every action so you can see exactly what happened and why.
In short
How is a custom AI agent different from a chatbot?
A custom AI agent does work, while a chatbot only answers questions. The agent reads an incoming request, looks things up in a CRM or database, decides the next step, and completes the task or prepares it for a person to approve. TechPit Solutions builds agents with OpenAI and Claude models, with narrow permissions, guardrails, human approval for sensitive actions, and a log of every action.
- Best for businesses with repetitive tasks that need some judgment, such as triaging support tickets or preparing quotes from emails.
- Built remotely after a free consultation and a use case and risk review, tested against real examples, then monitored and expanded once reliable.
Who this is for
- Businesses with repetitive tasks that need some judgment, not just fixed rules
- Teams already using automation that keep hitting cases a simple workflow cannot handle
- Operations managers who want work prepared for review rather than done from scratch
- Companies with data spread across a CRM, inbox, spreadsheets, and documents
- Owners interested in AI agents who want them built with proper safeguards
Problems we fix
- Staff spend hours reading, sorting, and acting on emails, tickets, and forms that follow familiar patterns
- Rule-based automations break whenever a request does not match the expected format
- Information needed for a task is scattered across several systems and has to be gathered by hand
- Earlier AI experiments produced impressive demos but nobody trusted them with real work
- There is no audit trail showing what an automated process did or why
What's included
What our Custom AI Agents service includes
Use case and risk review
We identify tasks where an agent adds real value, define what success looks like, and decide which actions the agent can take alone and which need a person to approve.
Agent design and tool access
We design the agent's instructions, the specific tools and API actions it can call, and the data it can read, keeping permissions as narrow as the task allows.
Memory and knowledge retrieval
Where needed, we give the agent memory of past steps or customer history and connect it to your documents and records so its decisions are based on your information.
Guardrails and approval steps
We add input and output checks, limits on actions, and human approval points in Slack, Teams, email, or a simple dashboard before sensitive actions go ahead.
Logging and monitoring
Every run is logged with inputs, decisions, tool calls, and results, with alerts on errors or unusual behavior, so you can review and trust what the agent does.
Documentation and handover
We document how the agent works, what it can access, and how to pause or change it, and train your team to review its work and handle exceptions.
Benefits
Why it's worth doing properly
Work that gets done, not just suggested
Agents can complete multi-step tasks across your systems, so routine work moves forward without someone stitching each step together.
Handles messy real-world input
Unlike rigid rules, an agent can understand varied emails, forms, and requests and still take the right action or flag them for review.
You stay in control
Approval steps, narrow permissions, and clear logs mean the agent works within limits you set and can be checked at any time.
Built around your process
Custom logic follows your rules, terminology, and systems, rather than forcing your team into the way a generic tool works.
Process
How the work runs
- STEP 01
Assess the task
We study how the work is done today, gather real examples, and agree on what the agent should do, what it must never do, and how results will be judged.
- STEP 02
Design the agent
We choose the model and platform, define tools, memory, guardrails, and approval steps, and confirm the design with you before building.
- STEP 03
Build and evaluate
We build the agent, connect it to your systems with limited permissions, and test it against real examples, including edge cases and attempts to misuse it.
- STEP 04
Monitor and improve
We review logs and approval decisions, fix mistakes, refine instructions, and expand what the agent handles once it has proven reliable.
Tools we commonly work with
- OpenAI API
- Anthropic Claude API
- n8n AI Agent
- LangGraph
- Model Context Protocol (MCP)
- Qdrant
- Supabase
- Langfuse
- Slack
- Microsoft Teams
FAQ
Custom AI Agents: common questions
What is the difference between an AI agent and a chatbot or automation?
A chatbot talks to people and answers questions. A standard automation follows fixed steps when a trigger happens. An AI agent combines both ideas: it can interpret varied input, decide which steps to take, use tools in your systems, and complete or prepare a task. In practice, many of our agents sit inside an automation workflow so the overall process stays predictable.
Is it safe to let an AI agent act in our systems?
It can be, with the right controls. We give agents only the specific actions they need, use separate accounts with limited permissions, require human approval for sensitive actions, and log everything. We also test how the agent behaves with unusual or misleading input before it goes live. If a task is too risky to automate, we will say so.
Should we use OpenAI, Claude, or something else?
Both OpenAI and Claude models work well for agents, and the better choice depends on the task, cost per run, and your data requirements. We often test more than one model on your real examples. If data cannot leave your environment, we can look at local models, which we cover in our private AI service, with the trade-offs explained.
Why build agents in n8n?
n8n lets us place an AI agent inside a visual workflow, so your team can see each step, connect hundreds of apps, and add approval points easily. It can be self-hosted for tighter data control. For more complex agents we may write custom code instead. We pick the approach that is easiest for you to understand and maintain.
What happens when the agent makes a mistake?
It will happen sometimes, so we plan for it. Approval steps catch mistakes before they reach customers, logs show exactly what went wrong, and alerts flag failed runs. We review errors, adjust instructions or tools, and re-test. Over time the agent handles more cases correctly, but important decisions stay with your team.
How much does a custom AI agent cost?
Cost depends on the task's complexity, the number of systems and tools involved, the approval and logging requirements, and how much evaluation is needed. Model usage and platform fees are separate and depend on volume. Book a free consultation and we will scope the agent and give you a clear quote.
Available in
Delivered remotely in these countries and across Europe. Prices in USD.
Learn more about Custom AI Agents
Terms explained
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A free 15-minute call to understand what's slipping through the cracks. You'll leave with the department that fixes it first and what it costs, whether or not you work with us.