Go to Market

Business leadership with AI Agents

Secure, intelligent transformation without barriers. We move from an AI that answers to an AI that acts.

An agent is no longer an option, it is a necessity to compete and stand out.

If an LLM is a "brain" capable of reasoning and planning, an AI Agent is that brain equipped with hands (tools) and a will of its own (autonomy). We give it a complex objective and it executes it.

BUSINESS BENEFITS OF DEPLOYING AI AGENTS

85%

Improved user experience and customer engagement

63%

Business growth after deploying generative AI

56%

Improved security posture reported by companies

45%

Improved employee productivity

WHY AI AGENTS WITH IZERTIS

End to end

A single partner from discovery to the day after. From feasibility auditing to continuous monitoring in production.

360º AI

Not just agents. Governance + Integration. We cover every dimension the business needs so that AI delivers real value.

Real experience

From failing to scaling. Real cases in production across different sectors. We reuse frameworks; we have seen the problems.

THE TIME IS NOW

Digital leaders are already creating value. Is your company ready?

The pace of technological change is what sets market leaders apart from those left behind. AI makes it possible to scale without resource constraints, improve the speed and quality of work, and turn business knowledge into new value-added services.

Talk to an expert

THE MARKET IN NUMBERS

79%

of companies already adopt AI agents

88%

plan to increase their AI budget because of the potential of agents

60%

reduction in low-value work

+3M€

in annual savings after deploying agents in low-value processes

30%

of the IT budget already goes to AI — core spend, not experimental

15%

of the total budget dedicated to agents in future-build companies

An application that achieves an objective by observing the world and acting with the tools at its disposal.

Operating model

Acts

Observes

Corrects

4 components that work together to turn an objective into a completed action.

Models

They reason, determine the plan and generate responses

Orchestration

Maintains memory, state, plan and available tools

Tools

Retrieve data and execute actions via APIs or services

Runtime

Runs the system when it is invoked

ANATOMY OF AN AGENT

LLM+PROMPTING

The brain and the rules

The LLM reasons and plans; prompting directs it with a role, objective and rules. Chain-of-Thought (CoT) forces step-by-step reasoning.

MEMORY

Short and long term

Short term: the conversation history, volatile. Long term: semantic vectors retrieved by search when needed.

KNOWLEDGE

RAG — Breaking barriers ahead of time

The organization's private data retrieved by semantic search and injected into the LLM on every query. The agent responds with its own knowledge without needing to retrain the model.

TOOLS+ACTION

The agent's hands and feet

APIs, functions, databases or other agents. A2A (agent-to-agent communication) and MCP (consumption of external services).

TYPES OF AGENT

OPERATIONAL AGENTS

The doing

They execute actions, make decisions and automate tasks in real time across complex digital or physical environments.

DATA AGENTS

The knowing

They search, filter, transform and synthesize large volumes of data. They generate insights and prepare information for other systems.

IZERTIS 360º AI APPLIED TO AGENTS

From concept to operation

Most AI solutions cover one or two aspects. Our 360º AI is born from a commitment to generate value across every dimension the business needs — with governance and monitoring from day one.

4 PILLARS OF VALUE

Productivity

→ Automation
→ Agile decisions

Security

→ Threat detection
→ Regulatory compliance

Efficiency

→ Processes 30% more agile
→ Resource optimization

User experience

→ Intelligent interfaces
→ Personalization

Talk to an expert See success stories →

Capital · Quality and stability

→ Continuous monitoring of conversational and functional quality
→ If quality drops 5%, we know within 24h, not in 3 months
→ Controlled stability and response times

Governance · Policies and traceability

→ Clear policies: what each agent can do and under what conditions
→ Traceable responsibilities: every decision and change is logged
→ Controlled operating cycle: updates without chaos

Monitoring · Behavior and usage

→ Behavioral quality: conversational coherence, accuracy, error correction
→ Tool usage: is it using the available APIs correctly?
→ Continuous improvement based on data and periodic reviews

360º MONITORING · AI GOVERNANCE THAT PROTECTS THE INVESTMENT

From uncertainty to control

An AI agent without governance or monitoring: problems are not visible until it is too late. With our 360º AI, agents scale with risk under control.

CONTINUOUS IMPROVEMENT CYCLE

Monitor

Alert

Improve

Optimize

THE PHASES OF OUR SUPPORT

Consulting

→ AI feasibility audit
→ Mapping of use cases with clear payback
→ Data validation and governance from the start

Design with an operational perspective

→ Agnostic architecture
→ Pre-selected models
→ API-first integration

Implementation without surprises

→ Iterative deployment
→ Integrated MLOps
→ Live testing

Governance and continuous monitoring

→ Drift and bias monitoring
→ Automatic retraining
→ Compliance auditing
→ Evolution without downtime

CHALLENGES AND DEPLOYMENT

Building agents is not easy. Putting them into production is even harder.

We know the challenges because we have solved them. That is why our agile approach reduces risk at every step of the way.

Development challenges

→ Cost management
→ Multi-agent systems
→ Security and privacy
→ Framework fragmentation
→ Consistency of results

→ Connection to data sources
→ Human in the loop
→ Interoperability
→ Multiple APIs
→ Output quality

Production challenges

→ Observability and traceability
→ Security and authentication
→ Spend monitoring
→ Session analysis
→ Interwoven workflows

→ AgentOps / RAGOps / LLMOps
→ Tool governance
→ Results that drift
→ Skill sets

THE AGILE PATH TO ENTERPRISE AI

Discover where AI generates the most value

Identify repetitive tasks where an agent can automate and create value: support, customer service, internal operations.

Validate that the current stack works

Infrastructure, systems and data the agent will interact with, and security and compliance requirements.

Test with low risk before scaling

Define the agent's objective, its flows and data sources, and validate the technical prototype.

Go to production without disruption

Deploy the agent in production, connect it with existing systems and configure monitoring and scalability.

Continuously optimize ROI

Metrics and feedback to improve the agent, expand capabilities and optimize automated processes.

We work with the leading providers and platforms of the AI ecosystem, with the ability to make agnostic choices according to the needs of each project.

VENDOR ECOSYSTEM

SAAS Applications

Chat GPT · Copilot · Einstein · Gemini Enterprise

Hyperscale Platforms

Azure · Google Cloud · AWS

Foundation models

Google · OpenAI · Anthropic · Meta

Infrastructure

NVIDIA · Snowflake · Databricks

SOLUTIONS BY LEVEL OF COMPLEXITY

Out of the box

Ready-to-use conversational assistants · Productivity copilots · Conversational analytics

Configurable

Agents on your data · Service and support flows · Vertical agents (sales, procurement)

Custom development

Agents with tools (APIs and functions) · RAG pipelines · Agents with memory for long-running processes

SUCCESS STORIES

From failing to scaling. Real cases in production.

Real experience deploying AI agents across different sectors, with different challenges and measurable results.

OPERATIONAL AGENTS · GOOGLE ADK · VERTEX AI · GEMINI

AI agents for automation in the travel sector

Centralized AI engine on Google Cloud that orchestrates autonomous agents for travel quoting, package scheduling and knowledge management. Three reusable agentic patterns: sequential pipeline, HITL and Agentic RAG.

→ Automation of quoting and scheduling
→ Knowledge management with conversational RAG
→ Quality control with Human-in-the-Loop at critical points
→ Secure connectivity with on-premise systems via MCP

DATA AGENTS · GOOGLE CLOUD · LOOKER · BIGQUERY

Conversational analytics in a booking platform

A leading tourism company. Agents integrated into Looker to query booking data in natural language, without technical knowledge.

→ Conversational queries in everyday language
→ Real-time answers on BigQuery data
→ Modular design scalable to new sources

AI AGENT · MS COPILOT · POWER BI · ML

Quotas and incentives agent for the sales network

Conversational agent on Copilot, with a Power BI dashboard and ML models to generate scenarios and predictions. Dynamic quota management integrated into existing permissions and roles.

→ 90% fewer manual requests
→ ROI under 7 months
→ +40 satisfaction points (out of 200)
→ Integrated into the existing permissions and roles flow

AI AGENTS · FOUNDATION MODELS · ON-PREMISE

On-premise regulatory compliance assessment

For an insurance sector regulator. Computer Vision and foundation models that validate whether product brochures comply with content and format regulations, deployed on-premise with no GPU or external APIs.

→ Automated validation of brochures
→ 100% on-premise, no external LLM APIs
→ Works with a low volume of labeled data
→ No document leaves the organization's perimeter