AI & Generative AI

AI agents & agentic workflows

Agents that use tools, follow policies and hand off to people, with guardrails, evaluation and observability built in.

Vue d’ensemble

AI agents can interpret a goal, plan steps, call tools and act on the results. Used well, they take on multi-step work that previously required someone to stitch several systems together: gathering information for a report, triaging requests, reconciling records or preparing a case for review.

We design agents for production rather than demos. That means a narrow, well-defined scope, a small set of reliable tools, clear policies on what the agent may and may not do, and explicit approval points where a person reviews an action before it is taken. Agents operate with least-privilege access and layered protections against prompt injection.

Every agent we build is observable and testable. We log reasoning steps and tool calls, build scenario-based evaluations that include edge cases and failure modes, and track task success, escalation rates and cost. Autonomy is then increased gradually as evaluation results and user trust grow.

Ce que vous obtenez

01Agent design with scope, tools, policies and human approval points
02Tool integrations with secure, least-privilege access
03Working agent or multi-agent workflow in your environment
04Scenario-based evaluation suite and end-to-end tracing
05Operating runbook covering escalation, monitoring and change control

Technologies que nous maîtrisons

LangGraph
OpenAI Agents SDK
Model Context Protocol (MCP)
Microsoft Copilot Studio
Amazon Bedrock Agents
LangSmith
OpenAI, Anthropic and Google model APIs

Nous sommes indépendants des éditeurs : les outils sont choisis en fonction de vos besoins et de vos investissements existants.

Les questions que l’on nous pose souvent

How much autonomy should an agent have?

Usually less than teams expect at first. We typically start with agents that recommend actions for a person to approve, then allow more autonomy for specific, low-risk steps once evaluation results show the agent performs reliably.

What happens when an agent gets something wrong?

Agents are designed so errors are caught and recoverable: actions above a risk threshold need approval, every step is logged and failures route to a person with full context. Each incident becomes a new test case, so the same issue is checked in every future release.