AI Agent Development
We design and build AI agents that complete real tasks inside your systems: they read the request, consult your data, use your tools (ERP, CRM, email, internal APIs) and hand the result to your team or escalate it when a decision needs a person. We start with one well-defined process, measure it and only then scale.
Who It's For
Companies that already know which process they want to delegate: a team answering the same requests every day, an operation that moves information between systems with judgment calls along the way, or a pilot that worked in a demo and never reached production. Ideal for COOs, operations directors and technology leaders who need an agent that works with their real data, their permissions and their controls, not a generic chatbot.
Deliverables
What You Gain
Tasks completed, not just answered
An agent does not stop at suggesting what to do: it checks the order, updates the record, drafts the reply and leaves the case ready for review. Your team receives finished work instead of another notification.
Clear limits and human approval where it matters
Each agent has an explicit list of tools, read and write permissions and actions that always require approval. If something falls outside its scope, it escalates with the context already gathered.
Every decision is traceable
We log which data the agent consulted, which tools it used and why it took each step. That makes audits, error reviews and continuous improvement possible.
Works with the systems you already have
We connect the agent to your ERP, CRM, databases and internal APIs through controlled integrations. You do not need to replace your stack to start.
Measurable results from the first process
Before building, we agree on the baseline: handling time, volume, error rate or cost per case. After launch we compare with the same metric, so the decision to scale is based on data, not on enthusiasm.
Your team keeps control and learns
We document the design, the prompts, the evaluation set and the operating rules, and we train your team to adjust them. The agent does not become a black box that only the vendor understands.
Best Practices
One process, one owner, one metric
The agents that reach production start small: a single process with a clear start and end, a business owner who validates the results and a metric that everyone agrees on. Broad "do-everything" assistants are hard to evaluate and harder to trust.
Evaluate with real cases before launch
Build a set of real cases, including the difficult ones, with the expected answer. Run it on every change to prompts, models or tools. Without this set, every adjustment is a guess and regressions reach users first.
Least privilege for every tool
Give the agent only the tools and permissions the process needs, and require human approval for irreversible actions such as payments, deletions or messages to customers. Treat every external input as untrusted to reduce prompt injection risk.
Operate it like any other system
An agent in production needs logs, cost and latency alerts, an owner on call and a way to turn it off. Review a sample of its decisions every week: that is where the improvements for the next version come from.
Patterns & Practical Cases
The agents that deliver value share a pattern: a clear goal, a short list of well-described tools, access only to the data they need and a defined point where a person reviews or approves. They usually begin by assisting a team (preparing the work) and only take autonomous actions once the evaluation shows they are reliable.
Operations and back-office agents
Typical cases: an agent that receives supplier requests by email, checks the order in the ERP, validates it against the contract terms and prepares the response; an agent that classifies support tickets, gathers the customer's history and proposes the resolution; or an agent that reconciles records between two systems and only escalates the differences it cannot explain.
Agents for sales and internal teams
An agent that prepares a meeting brief with CRM data and recent interactions; one that drafts quotes from the price list and the approval rules; or one that answers internal questions about policies and procedures and opens the corresponding request. In all of them the person decides, and the agent saves the research and preparation time.
Key patterns
Technology Ecosystem
AWS managed service to build agents on foundation models, with tool calling, knowledge bases, guardrails and access control integrated with IAM. A good fit when your data and systems already live on AWS.
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The standard language for agent logic, integrations and evaluation. Frameworks such as LangGraph or the providers' SDKs let you define steps, tools and state explicitly and test them like any other code.
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Open-source automation platform with AI agent nodes. Useful for agents that orchestrate many SaaS tools, with visual flows your team can maintain and the option to self-host to keep control of the data.
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Microsoft's platform to build and operate AI agents and applications on Azure, integrated with Entra ID and the Microsoft 365 ecosystem. A natural option for organizations that already work on Microsoft.
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Google Cloud's AI platform, with Gemini models and tools to build agents connected to BigQuery and Google Workspace data. Recommended when your analytics and data already run on Google Cloud.
Learn more →Do you enjoy building AI that works in production?
We are looking for engineers who combine software fundamentals with experience in LLMs: integrations, evaluation, security and operation. If you prefer an agent that works every day over an impressive demo, we want to meet you.
What to Avoid
Starting with the technology instead of the process
Choosing a framework or a model before defining which task the agent will solve leads to impressive demos with no owner and no metric. First define the process, the expected result and how you will measure it; the technology comes after.
Giving the agent more permissions than it needs
An agent with write access to everything is a security risk: a malicious instruction hidden in an email or a document can trigger actions nobody approved. Limit tools, separate read from write and require approval for irreversible actions.
Launching without an evaluation set
If you do not have real cases with the expected answer, you cannot know whether a change improved or broke the agent. The problems show up in front of customers, and the team ends up distrusting the tool.
Ignoring the cost per task
Agents make several model calls per task, and the cost grows with volume. Measure the cost per completed task from the pilot, set limits and alerts, and compare it with the current cost of the process before scaling.
Which process would you delegate to an agent?
THAT
COMPLETES
THE TASK, NOT
JUST ANSWERS