Data & AI
AI agents: automating tasks on top of your systems, with guardrails
An AI agent is a program that uses a language model to decide on a sequence of actions and execute them by calling tools: read a ticket, look up a customer in the CRM, create an order in the ERP, send an email. Unlike a simple conversational assistant, it acts on your systems, which makes it both powerful and demanding.
This service is for businesses with repetitive processes that involve several tools and a bit of judgment: qualifying support requests, preparing quotes, following up on incomplete files, reconciling data between applications. We design semi-autonomous agents first, with human validation on sensitive actions, then widen autonomy as measurements justify it.
We pay particular attention to guardrails: a closed list of tools with minimal permissions, irreversible actions subject to confirmation, budget and loop limits, full logging, protection against prompt injection, and evaluation on real scenarios before any production rollout.
When do we step in?
First-level customer support
Requests arrive by email, form or chat. An agent reads the request, checks the customer's history, searches the knowledge base, proposes a reply or creates a qualified ticket, and hands over to a human as soon as it is unsure.
Multi-tool case processing
A request involves checking a piece of information in the CRM, entering another in the ERP and sending a confirmation email. The agent chains these steps and stops for validation on binding actions.
Follow-ups and reminders
Incomplete files, unpaid invoices, unanswered quotes: the agent identifies the cases, drafts a context-appropriate follow-up and submits or sends it according to the defined rules.
Internal assistant for teams
Your staff ask in natural language for a sales status, a summary of a customer account or the creation of a task. The agent queries the right systems with the permissions of the logged-in person.
Existing agent that is unreliable
A quickly built agent sometimes does anything: loops, unwanted actions, made-up answers. We take it over with guardrails, an evaluation and a readable architecture.
How we work
- 1
Process mapping
We describe the current process, the tools involved, the decision rules and the edge cases. We identify which steps can be entrusted to the agent and which must remain human.
- 2
Designing the agent's tools
Each action is exposed as a precise tool, with validated parameters and minimal permissions: look up a customer, create a draft, never generic access to the database or the API.
- 3
Evaluation scenarios
We build a set of real cases, including tricky ones and injection attempts, with the expected behavior. The agent is measured against these scenarios at every change.
- 4
Development and guardrails
Orchestration, error handling and retries, turn and budget limits, human confirmation on irreversible actions, logging of every decision and tool call.
- 5
Gradual rollout
The agent first runs in suggestion mode, then with human validation, then autonomously on the cases where its measured success rate is sufficient. Each step is decided with you.
- 6
Monitoring and improvement
Dashboard of actions, failures, costs and human interventions. Failure cases feed the evaluation set and the improvements.
Technologies we use
- Anthropic Claude, OpenAI, Mistral
- Model Context Protocol (MCP)
- LangGraph
- OpenAI Agents SDK
- Python (FastAPI)
- Node.js (TypeScript)
- Temporal or task queues (BullMQ, Celery)
- Langfuse
- HubSpot, Salesforce, Zendesk, Odoo (API)
- PostgreSQL and Redis
Related expertise
Why choose Agencei?
Gradual autonomy, never imposed
We start in suggestion mode and only widen autonomy based on measured results. You stay in control of what the agent can do on its own.
Tools designed like APIs
Each agent tool is a precise, validated and logged function, with the same security requirements as an API exposed to third parties.
Built-in security
Prompt injection through the content it reads, minimal permissions, budget limits, irreversible actions under confirmation: these protections are planned at design time.
Real integration with your systems
We connect the agent to your CRM, ticketing, ERP and databases through their APIs, handling authentication, quotas and errors, because that is where most agent projects fail.
In brief
- What is this service?
- Design and deployment service for AI agents, autonomous or semi-autonomous, that carry out tasks by calling your business tools (CRM, ticketing, ERP, databases) with guardrails and human oversight.
- Who is it for?
- Businesses with repetitive processes that involve several tools and some interpretation, for example customer support, sales administration or case management.
- What problem does it solve?
- Time-consuming manual multi-tool tasks, queues of requests to qualify, unreliable or dangerous prototype agents, need to automate without losing control.
- How long does it usually take?
- A first agent in suggestion mode on a limited scope often takes a few weeks. Extending autonomy and integrations then happens gradually over several weeks or months.
- What factors influence the price?
- Price depends on the number of tools and systems to connect, process complexity, the targeted level of autonomy, security and compliance requirements, and the volume of evaluation scenarios to build.
- How does the engagement run?
- Process mapping, design of tools with minimal permissions, evaluation scenarios, development with guardrails, gradual rollout (suggestion, validation, autonomy), continuous monitoring.
- What are the risks?
- Unwanted or irreversible actions, prompt injection through the content read, costly loops, data leakage, dependency on a model provider, excessive trust in an unevaluated agent.
- What alternatives exist?
- Conventional rule-based automation when the process is deterministic, a conversational assistant with no action on systems, or human processing with better tools.
Frequently asked questions
Can an AI agent make serious mistakes?
Yes, if it has too much power and no guardrails. That is why we limit its tools, require confirmation for irreversible actions, cap its loops and budget, and roll it out gradually. A well-designed agent makes recoverable mistakes, never mass deletions or uncontrolled sends.
What is the difference between an AI agent and conventional automation?
Conventional automation follows explicit, predictable rules. An agent uses a language model to interpret a situation and choose the steps, which lets it handle varied cases but makes it less predictable. When the rules are clear, conventional automation is preferable; an agent is justified when judgment over language is needed.
How does the agent access our tools?
Through their APIs, with dedicated credentials holding minimal permissions, or via MCP servers that expose precise tools. Every call is logged. The agent never has direct database access or administrative rights.
What is human-in-the-loop validation?
It means having a person approve certain agent decisions or actions before they are executed: sending an email, creating an order, modifying a record. It is systematic at first, then reserved for sensitive or uncertain cases once reliability has been measured.
How do you measure whether an agent works?
With a set of real scenarios and expected behaviors, replayed at every change, and in production through the rate of tasks completed without intervention, the escalation rate and user feedback. Without these measurements, nobody can claim an agent is reliable.
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