AI agents that support your operations.

Connect knowledge with action

Finding information, drafting a response, updating a record or coordinating several tools: AI becomes useful when it fits the way your teams work. Altodia helps you design assistants and agentic systems connected to your business applications, with measurable objectives and a clearly defined scope of action.

Generative AI creates and transforms content. An agentic system combines a model, context, tools and execution logic to carry out a task across multiple steps. An agent can choose an action, observe its outcome and adjust what happens next, within the boundaries your organization sets.

We help you choose the right level of autonomy: an on-demand assistant, a predefined workflow or an agent that can adapt its approach. We introduce multiple agents when dividing responsibilities brings a clear benefit.

Practical applications for your teams

• Knowledge and internal support — search your procedures, find relevant sources and prepare an answer your team can verify.
• Customer relationships and sales — summarize account history, prepare a meeting or draft a proposal, then route proposed actions to the right person.
• Operations and back-office work — gather documents, identify missing information and prepare updates to your business applications.
• IT and engineering — bring documentation, tickets and technical events together to support diagnosis and suggest next steps.

The building blocks of an agentic system

RAG: answer questions using your knowledge

RAG, or Retrieval-Augmented Generation, retrieves information from a collection of sources and supplies it to the model when generating a response. We work on document preparation, hybrid search and result ranking to select relevant passages.

The aim is to produce answers supported by accessible sources, with explicit controls for content freshness and access permissions. RAG does not retrain the model or eliminate errors: answer quality still needs to be evaluated.

MCP: connect agents with your tools

MCP, the Model Context Protocol, standardizes how AI applications access tools and data exposed by compatible servers. Looking up a record, searching a knowledge base or creating a ticket becomes an explicit agent capability.

Depending on your environment, we use native connectors, direct APIs or existing or custom MCP servers. Each integration defines the available functions, authentication and permissions: having an API does not automatically make a system MCP-compatible.

A2A: help specialized agents work together

The A2A, or Agent2Agent, protocol allows compatible agents to exchange requests, track task progress and share results, including across different platforms.

It complements MCP: MCP supports access to tools and data; A2A supports communication between agents. We define responsibilities, shared information and delegation rules so that collaboration remains understandable and controlled.

Harness: manage how an agent runs

An agent harness is the software layer that manages execution around the model: calling tools, handling state and deciding when work should continue or stop. It provides the structure an agent needs to work across multiple steps and sessions.

We build in controls suited to the project: time and cost limits, error handling, recovery checkpoints, human approvals and execution traces. Action isolation and protection of credentials complement these controls at the infrastructure level.

Context engineering: provide the right information at each step

Context engineering organizes what the model receives at each step: instructions, selected documents, available tools, relevant history and permitted memory. We shape this context to reduce noise and retain the information the task needs.

Model selection, prompt engineering and, where evaluations justify it, fine-tuning complete the design, guided by your requirements for quality, confidentiality, response time and cost.

Measure quality and stay in control

An agent should be assessed on both the task outcome and the actions it took. We define representative evaluation scenarios and track task success, faithfulness to sources, human corrections, response times and cost per operation.

Production readiness includes permissions limited to what is needed, approvals for sensitive actions, protections against prompt injection and a way for a person to take over. Observability and LLMOps / AgentOps practices then support version tracking, regression detection and improvements based on real usage.

From the first use case to production

1. Define the value — choose a process, its users, its constraints and the measures that will guide the next decision.
2. Design the architecture — establish the data, models, integrations, level of autonomy and human responsibilities.
3. Evaluate a pilot — test within a limited scope, compare results with the current process and refine the system with your teams.
4. Deploy and hand over — prepare deployment, operations, documentation and training to support lasting adoption.

Have a process to simplify or an existing agent to improve? Let’s discuss your use cases and environment and identify a practical starting point.