AI Agents

AI Agents for Product Teams.

Build AI agents that automate real workflows, with guardrails, evaluation, and production reliability. Product teams do not need "Agent Demos." They need agents that connect to tools, execute tasks safely, and improve outcomes without breaking operations.

Logiciel builds agentic AI systems for product teams that want to ship faster, reduce manual ops, and scale execution across workflows.

Built for production delivery Secure by design Measurable outcomes

Talk to an AI Agents Expert

63%

Manual effort reduced across workflows

3×

Faster decision and action cycles

24/7

Always-on, autonomous execution

2011

Building production software, since

Who this is for

Who this is built for.

AI agents deliver the most for teams ready to move a real workflow into production - and they aren't the right call for everyone.

Ideal for Not ideal for

A CTO or Head of Product automating repeatable workflows across teams.

A SaaS team shipping AI features that need reliability, evaluation, and control.

An operator-led business with multiple workflows and tool fragmentation.

A team tired of "automation scripts" and ready for intelligent execution.

You want a flashy "agent demo" rather than a workflow shipped to production.

You expect fully autonomous agents with no guardrails, review, or oversight.

You have no clear workflow or success metric to target yet.

You need a one-off script, not a governed, evaluated system.

What we build

What we build.

Workflow Agents

Agents that execute tasks across systems, not just answer questions.

Ticket triageFollow-up automationDocument routingQuote generationInternal approvals

Tool-Using Agents

Agents that call APIs, update records, trigger workflows, and coordinate actions.

CRM updatesProject actionsOps automationAnalytics retrieval

RAG + Knowledge Agents

Agents that retrieve from internal knowledge with citations and controlled outputs.

Policy agentProduct docs agentOnboarding agent

Multi-Agent Systems

When one agent is not enough. We design rules-based agents with routing and governance.

Support agentFinance agentOperations agentShared memory

Fastest ROI

Where AI agents deliver the fastest ROI.

01 · Support

Customer & Support Workflows

Automates triage, routing, and response drafting - escalating issues with full context to reduce backlogs without adding headcount.

Triage & repliesBacklog ↓

02 · Revenue

Revenue & Operations Workflows

Streamlines follow-ups, qualification, approvals, and proposal drafts to accelerate execution while maintaining control.

Follow-ups & approvalsCycle time ↓

03 · Delivery

Engineering & Delivery Workflows

Automates release notes, incident context, runbooks, and documentation to improve execution hygiene across teams.

Docs & runbooksKept current

Engagement models

Engagement models.

Discovery Sprint (5 to 10 days)

Best when you need clarity before building.

OutputsWorkflow selection, feasibility validation, data readiness, architecture plan, evaluation plan.

Build Sprint (2 to 4 weeks)

Best when you're ready to ship a workflow to production.

OutputsWorking agent, integrations, controlled actions, evaluation checks, production rollout plan.

Dedicated Agent Team (monthly)

Best for multi-agent roadmaps and continuous iteration.

OutputsRoadmap execution, agent improvements, new workflows, monitoring, tuning, reliability upgrades.

Embedded Advisory (part-time)

Best when your internal team builds and you want senior oversight.

OutputsArchitecture reviews, evaluation design, guardrails, rollout planning.

Process

Process and delivery timeline.

Workflow Scope and Success Metrics (Week 0 to Week 1)

Select the workflow that matters most.
Define success metrics and failure conditions.
Map data inputs, systems, permissions, and approvals.
Decide what requires human review.

Architecture and Evaluation Design (Week 1 to Week 2)

Choose the right agent pattern (RAG, tools, routing, memory boundaries).
Define evaluation checks (quality, safety, regressions).
Set governance rules (audit logs, role-based actions, escalation).

Build and Integrate (Week 2 to Week 3)

Implement the agent and tool integrations.
Build guardrails and safe action boundaries.
Add validation, error handling, and fallback flows.

Launch and Observe (Week 3 to Week 4)

Production rollout with monitoring and feedback.
Track agent performance and workflow completion rates.
Iterate quickly based on real usage signals.

Why Logiciel

Why Logiciel for AI agents.

Reliable delivery, not experiments

We build production-ready systems with clear ownership, predictable execution, and outcomes aligned to real workflow goals.

Built with governance from day one

Every agent is designed with traceability, permissions, and safe escalation so decisions stay auditable and controlled.

Built to scale across workflows

Our foundations support adding new agents and workflows without re-architecting the system.

Proof & credibility

Proof and credibility.

Production-grade delivery across complex workflows

Logiciel has delivered systems used in high-usage environments where reliability matters daily. AI agents are no different, the same engineering discipline applies.

See Success Stories

Ready to automate a workflow that actually matters?

If you tell us about the workflow, we'll let you know what's feasible, what data is required, and what a production rollout would entail.

Talk to a Senior Engineer

FAQ

Extended FAQs.

What is the difference between an AI agent and automation?

Automation follows fixed scripts. An AI agent reasons over context, decides which tools to call, and adapts to the situation, within the guardrails you define, so you get flexibility without losing control.

How do you prevent hallucinations?

We ground agents in your own data with retrieval and citations, constrain their outputs, and add evaluation checks plus human review on high-stakes steps. Anything unverified is flagged, not acted on.

Do we need perfect data to start?

No. We start with the data you have, add validation and fallbacks for the gaps, and improve data quality alongside the rollout. Readiness is assessed in the discovery sprint.

Can agents take actions in our systems safely?

Yes. Actions run through scoped permissions, safe action boundaries, audit logs, and approval gates, so an agent can only do what you have explicitly allowed.

How do you measure success?

Against the workflow metrics we define up front, completion rate, accuracy, time saved, and error/escalation rates, tracked with monitoring after launch.

Let's build

Ready to Put AI Agents Into Production Safely.

Bring us a workflow worth automating and we'll pressure-test what's feasible, scope a production-ready agent, and ship it with the guardrails, evaluation, and oversight to run it safely at scale.

Talk to a Senior Engineer