AI Agents
AI agents that specify, code, test and document across the whole delivery cycle. Our principle: AI writes the code, the engineer signs it off.
An agent is not a chatbot.
It reasons, plans, acts.
A production-ready AI agent is not an isolated copilot: it is a system that reasons, breaks down a task, calls your APIs and is accountable. Adservio installs the governance layer that makes these systems auditable and defensible.
What We Do
Executable specification
ASDD in four steps: specify intent and acceptance criteria, plan the trajectory, split into parallel and reversible batches, implement. The specification becomes the source of truth, not a document that goes stale.
On recurring cases, ready-to-adapt business templates avoid starting from scratch, and a low-code interface lets you iterate on the prompt, the tools and the guardrails without launching a development cycle.
- Method
- ASDD, four steps
- Deliverable
- Executable specification
How We Work
Discover
Map the use cases, the data and the regulatory constraints. Prioritise by value and feasibility, then write the executable specification.
depending on scope, sector and the level of compliance required
- Audit of use cases and pain points
- Value / feasibility matrix
- Executable specification (ASDD)
MVP
Deliver an agent in a real environment on the chosen use case, backed by tests, and measure the gap to target before going further.
depending on system complexity and integrations
- An agent in a real environment
- Generated tests, measured coverage
- Go / no-go before industrialisation
Scale
Industrialise and multiply use cases on a shared foundation. Integrate into pipelines, orchestrate agents together, train the teams.
depending on the number of agents and connected systems
- Multi-agent orchestration on a shared foundation
- CI/CD and MLOps integration
- Team upskilling
Run
Operate, maintain, optimise. Govern usage and cost, anticipate model shifts and regulatory change.
service commitment defined with you
- LLMOps observability
- FinOps optimisation of AI costs
- Continuous compliance audit
Our AI Agents experts
The team that supports you, from framing to run.
Sarah Abdallah
AI Project Lead
Sarah leads Adservio's GenAI projects, from framing to deployment. She aligns business, data and delivery to turn every use case into measurable value.
Jérémy Ruas
Senior Delivery Manager
Jérémy orchestrates the delivery of GenAI platforms : sovereign RAG foundation, LLMOps and industrialisation. He ensures reliable go-lives delivered on time.
Ezéchiel Amen Agbla
Practice Manager
Ezéchiel structures Adservio's GenAI practice : team skills development, augmented engineering standards and governance of agents in production.
Four Building Blocks
Agent Builder
Ready-to-customise business templates, low-code interface to iterate quickly on prompt, tools and guardrails.
Function calling & APIs
Typed JSON schemas, integration with your internal APIs, fallbacks and retry policies for reliable calls.
Multi-agent
Orchestration, delegation and synthesis between specialised agents. Hierarchical or peer-to-peer architectures.
Observability
Structured logs, metrics, real-time debugging, session replay. Your agents are auditable end to end.
Platforms in production
A sovereign AI foundation across the software lifecycle
−30% engineering cycles · 40+ agents in the catalogue
The operator of the French gas distribution network. The IT department must equip the four stages of the software lifecycle, specification, development, test and operations, without giving up control of its data or its AI Act compliance.
A sovereign AI platform that GRDF owns, a catalogue of more than 40 agents built up pillar by pillar, and test coverage raised by 50% with no data leaving the perimeter.
Depositary control augmented by AI
99.95% platform SLA · −70% reporting time
Under UCITS V, AIFMD and the ACPR's reinforced requirements, BPSS had to absorb exponential volume growth and process tens of thousands of daily NAVs, with legacy tooling that had become a regulatory risk.
A cloud-native platform driven by a business Meta-Language, where compliance becomes configurable and AI speeds up anomaly detection, delivered with zero blocking incidents in production.
Real-time analysis of video streams
−58% waiting time · +26% satisfaction
Fifteen million visitors a year. Optimising FastPass slots and personalising offers means reading the flows in real time, without ever stepping outside the GDPR framework.
Video analysis running on edge inference, personalised ad hoc offers and flow management that cuts queueing at the attractions, GDPR by design.
Insights & Perspectives
The Model Context Protocol: Beyond the Hype, the Standard for AI Agents
Architecture, 2026 specification, official registry, security: how the Model Context Protocol went from hype to the production standard for AI agents.
Autonomous SRE in 2026: agentic incident response
AI agents wired to observability correlate telemetry, code and deployments to triage and remediate incidents. Alert fatigue down 40-60%, MTTR falling.
Agentic AI at Work: How Autonomous Agents Are Transforming Organizations
Explore how agentic AI systems are revolutionizing the world of work by automating complex tasks and augmenting human capabilities.
Deploy your AI agents in production
Tell us where you stand. An Adservio expert gets back to you within 24 business hours.
Frequently asked questions
A chatbot answers a question with text. An AI agent pursues a goal: it reasons, breaks the goal into steps, calls the tools and APIs it needs, checks its own result and starts again if it failed. The difference is not the model but the ability to act on your systems.
Framing takes 2 to 6 weeks depending on scope, sector and the level of compliance required. A first agent runs in a real environment after 4 to 10 weeks. Industrialising several use cases on a shared foundation then takes 3 to 6 months.
Through four layers: validating each action before it runs, scoped permissions, sandboxed execution and human intervention on sensitive decisions. No code reaches production without an Adservio engineer's review. The AI writes the code, the engineer signs it off.
MCP is an open standard that describes how an agent connects to a data source or a tool. Instead of writing one integration per system, you expose an MCP server and every compatible agent can use it. It cuts integration cost and avoids being locked into a single vendor.
Through your existing APIs, an MCP server or a connector built for the occasion. The agent never queries a database directly: it goes through an interface that carries the permissions, logs the calls and can be revoked without touching the agent itself.
Compliance depends on the use case, not the technology. It requires a register of authorised models and tools, classified data, traceability of decisions and evaluation sets that can be replayed. These elements are built in from the framing stage, not added afterwards.
Yes. A hybrid gateway routes each request according to data sensitivity: sovereign or on-premise models for confidential data, external providers for the rest. Catalogues of several dozen agents run this way, with no data leaving the company's perimeter.