Our methodology
AI agents that don't lie.
Our approach transforms prompts into verifiable, traceable, auditable systems — built for production, not demo.
The problem
A prompt is not enough.
A well-written prompt produces an impressive demo. But in production, facing hundreds of real cases, hallucinations, context drift and the absence of traceability turn a promising tool into an operational risk.
Naïve prompt
- ✕Unverifiable output — no sources cited
- ✕Silent hallucination — invented APIs, wrong facts
- ✕Drift in long sessions — context gets polluted
- ✕Advisory guardrails — the model can ignore them
- ✕Variable results day to day (±8-14%)
- ✕No audit possible — black box
Studio CodeAI Architecture
- ✓Every claim traceable to its primary source
- ✓Up-to-date documentation injected in real time (MCP)
- ✓Context isolated per sub-agent — no cross-contamination
- ✓Deterministic guardrails — non-bypassable by the model
- ✓Reproducible result — reliability is in the architecture
- ✓Safety escalation — 'I don't know' rather than lying
Our philosophy
Five pillars for reliable agents
We don't engineer the model — it's frozen. We engineer what reaches it and how many times it self-corrects.
01
Context, not prompt
Quality depends on the assembled context (5,000 to 50,000 tokens), not the 6 words you type. We build the effective context with precision.
02
Context budget
More context ≠ better. We load the right information at the right time (just-in-time), never in bulk — to keep the signal clean.
03
Reliability outside the model
What must be reliable doesn't depend on the model's goodwill. Tool restrictions, deterministic hooks, validation schemas — non-bypassable.
04
Architectural verification
Every output is cross-checked against a source of truth. Verification is a step built into the loop, not a pious intention.
05
Hierarchical provenance
Primary source > curated source > model memory. Every claim is traceable. The agent that finds no source doesn't answer — it escalates.
The process
From pre-sales to follow-up — 8 steps, 2 gates
Each step produces a traceable deliverable. The two GO/NO-GO gates are mandatory stops — nothing moves forward until they are cleared.
Pre-sales & commercial framing
Need qualification, profitability filter (is the error costly? is the task repetitive? is the output verifiable?), proposal and project charter drafting.
→ Proposal + signed charterInterviews & discovery
Business interviews with end users. Structured scoping questionnaire: what is the source of truth? what is the cost of an error? what happens in case of doubt? how do we verify an output?
→ REQUEST (scoping document)Feasibility & philosophy validation
Definition of source of truth, provenance schema, necessary deterministic guardrails. Technical feasibility analysis.
→ RESEARCH (feasibility analysis)GO / NO-GO — Scoping
Does the case tick the 3 conditions? Is the source of truth accessible? Are the guardrails implementable? If not — we say so. An honest NO-GO protects the client as much as us.
→ GO / Conditional GO / NO-GO verdictAgentic architecture design
Decomposition into single-responsibility agents (parser, validator, writer). Choice of deterministic layers, output schemas, model routing, iteration plan.
→ PLAN (architecture + guardrails)Technical build
Implementation of the .claude/ tree (agents, commands, skills, settings), MCP wiring for up-to-date documentation, deterministic hooks, persistent agent memory.
→ Functional agentVerification & acceptance
Canary test suite, provenance check (does each output point to its source?), escalation test (does the agent properly refuse when in doubt?), pre-delivery review checklist.
→ Acceptance reportAcceptance validated
Does the agent fail safely? Is provenance traceable? Are guardrails deterministic (not just guidelines)? If a single criterion fails — we fix before delivering.
→ Delivery green lightDelivery
Three combinable modes depending on your context: installation on your workstations (autonomy + training), repository/template handover (integration + documentation), or Studio CodeAI-managed hosting (operated service + SLA).
→ Deployed agent + documentationFollow-up & continuous improvement
Quality monitoring, daily canary suite, agent memory that capitalizes on real cases, quarterly iterations. The agent improves in operation.
→ Follow-up report + iterationsDecision gates
GO / NO-GO — three concrete examples
The GO/NO-GO gate is the most valuable step in the process. It prevents building on sand — or delivering an agent that lies. Here's how it works in practice.
GO
Supplier invoice extraction
An accounting firm processes 2,000 invoices/month. Manual extraction costs 3 FTE.
- Costly error? Yes — a wrong amount skews accounting
- Repetitive? Yes — same schema on every invoice
- Verifiable? Yes — each field points to a passage in the source PDF
→ Three conditions met. We build.
CONDITIONAL GO
Customer support on document base
A SaaS publisher wants an agent that answers customer questions citing the documentation.
- Costly error? Yes — a wrong answer creates liability
- Repetitive? Yes — the same questions come back
- Verifiable? Partially — the documentation base is incomplete
→ Condition: structure and complete the documentation base first. Then we build.
NO-GO
Creative brainstorming for a campaign
A marketing agency wants an agent that generates viral campaign ideas.
- Costly error? No — a bad idea is filtered at sorting
- Repetitive? No — each brief is unique
- Verifiable? No — creativity has no source of truth
→ A structured agent would be over-engineering. A good prompt suffices here. We say so.
The architecture
What we deploy in practice
Every delivered agent relies on a standard directory structure. No black box — everything is readable, versioned, and auditable by your technical team.
.claude/ ├── CLAUDE.md ← mémoire du projet (<200 lignes) ├── agents/ │ ├── parser-agent.md ← extraction structurée │ ├── validator-agent.md ← confronte chaque sortie à la source │ └── writer-agent.md ← rédaction conforme ├── commands/ │ └── orchestrator.md ← point d'entrée, orchestre le flux ├── skills/ │ ├── data-fetcher/ │ │ └── SKILL.md ← récupération de données (préchargé) │ └── output-generator/ │ └── SKILL.md ← génération de la sortie vérifiée ├── rules/ │ └── validation.md ← règles chargées à la demande (paths:) ├── hooks/ │ └── scripts/ ← vérifications déterministes (lint, tests) ├── settings.json ← permissions, outils autorisés/bloqués └── .mcp.json ← connexions doc à jour (Context7, etc.)
The flow: Command → Agent → Skill
A command orchestrates, an agent executes in isolated context with its preloaded skill, an independent skill produces the output. Each component has a single responsibility — as in software engineering.
Command
orchestrates the flow
Agent
executes + preloaded skill
Skill
produces verified output
The mental model
Where your real leverage lies
The model's weights are frozen and naturally vary by ±8-14% from day to day. Your prompt represents only a fraction of the context seen by the model. Your real levers are above.
Deterministic guardrails
non-negotiable reliability
Iteration loop + verification
your biggest lever
Assembled context
big lever, source of hallucinations
Your prompt
small lever
Model weights
frozen · noise ±8-14%
In summary
From demo to product
A naïve prompt produces an impressive demo. Our architecture produces a deployable system — reproducible, auditable, that fails safely rather than lying. That's the difference between a prototype and a product your teams use with confidence.
Reproducibility
The same process produces the same quality, regardless of the model's variance.
Auditability
Every output is traceable to its source. GDPR-compatible and internal audit-ready.
Graceful degradation
The agent escalates instead of inventing. It fails safely, never silently.
Cost control
Light model for mechanical tasks, powerful model for judgment. Every token is invested.
Une stratégie initiée par Shayan Rais, validée par Boris Cherny — Créateur de Claude Code