Systems analyses · Field traces
Seven multi-agent systems, in production.
Seven systems designed, built or operated under my responsibility. Each entry is documented through its architecture, primitives, governance and operated impact. No logos, no staged testimonials: clients remain confidential by default.
Two of these systems were recognised at the GoHighLevel Global AI Agents Competition 2025 — one of the rare international competitions to rank multi-agent architectures actually in production, on the Chat and Voice categories.
01 · Multi-Agent · Chat
24/7 Multi-Agent Chat Booking
WinnerGoHighLevel Global AI Agents Competition 2025
Autonomous multi-agent chat system that qualifies visitors, answers questions, handles objections and drives appointment booking 24/7. Built for business services with human-grade precision requirements.
Role · Architect · Build · Run
- Context
- Inbound acquisition uncovered at night and weekends; recurring objections poorly handled by static chatbots; sales calendar saturated with unqualified conversations.
- Design
- Split into specialised agents (greeting, qualification, objection handling, booking) with explicit interaction contracts. Short session memory, persistent business memory for product rules, typed human escalation on doubt signal.
- Governance
- Each booked appointment is traced with the conversation that drove it. Product rules live in a governed store, not in the prompt. Editorial changes go through review before deployment.
- Outcome
- Inbound acquisition stabilised outside business hours. Sales calendar filled without manual work. Short editorial review cycle.
Primitives
- Agent mandate
- Qualification contract
- Objection ledger
- Booking handoff
- Escalation signal
Observed in production
- 40–120Qualified appointments / month
- 50–70 %Chat workload reduction
- 24/7Continuous qualification
02 · Multi-Agent · Voice
24/7 Multi-Agent Voice AI Ops
FinalistGoHighLevel Global AI Agents Competition 2025
Natural-language voice agents that answer inbound calls, route requests, handle support, book appointments and escalate to a human when context requires it.
Role · Architect · Build · Run
- Context
- Saturated phone reception, missed out-of-hours calls, heterogeneous answer quality across operators, response cost linear to volume.
- Design
- Real-time voice loop split into agents (greeting, intent, action, escalation). Sub-second target latency. Explicit boundary between autonomous actions (lookup, booking) and sensitive actions (contractual commitment, payment).
- Governance
- Each call leaves an indexed transcript. Sensitive actions are never executed without human validation or explicit contract. Latency and quality measured by cohort.
- Outcome
- No more lost calls outside hours. First-line support largely autonomised. Human escalation concentrated on cases worth the effort.
Primitives
- Voice envelope
- Intent classifier
- Action capability
- Escalation route
- Replay key
Observed in production
- 70–90 %Response time acceleration
- 30–50 %Support workload reduction
- 24/7Qualification and booking
03 · Lifecycle · Human + AI
Hybrid Human + AI Appointment Lifecycle
Full lifecycle — booking, reminder, meeting, bilingual recap, follow-up, inbound triage — orchestrated by AI but validated by human on committing actions.
Role · Architect · Build · Run
- Context
- Firms and executives overloaded by relationship admin: manual recaps, forgotten follow-ups, untriaged inbound, scattered notes. Premium experience promise hard to keep.
- Design
- Event-driven pipeline: each lifecycle milestone triggers an agent. Automatic bilingual recap (FR/EN), scheduled follow-ups, AI triage of inbound forms with human-validated email drafts.
- Governance
- No committing email is sent without human review. Every step is auditable. Sensitive wording is templated and versioned.
- Outcome
- Zero forgotten follow-up. Predictable, premium client experience. Drastically reduced admin load on the team side.
Primitives
- Lifecycle event bus
- Bilingual recap
- Follow-up scheduler
- Inbound triage
- Human draft validation
Observed in production
- 0Forgotten follow-up
- FR/ENInstant bilingual recap
04 · Media · Source-to-Artifact
Automated AI Video Production Engine
URL → video → social publications pipeline. A web source becomes a publish-ready media format in minutes, without manual intervention.
Role · Architect · Build
- Context
- Manual video content production, slow, costly, heterogeneous across brands and formats. High demand for multi-platform output, low execution capacity.
- Design
- Agent chain: source extraction, scripting, voice, editing, per-platform formats, scheduling. Each step produces a validatable artifact.
- Governance
- Human remains final editor on committing segments. Rights, sources and attributions are tracked at each step.
- Outcome
- Output capacity multiplied. Editorial consistency preserved. Time-to-publish reduced by an order of magnitude.
Primitives
- Source asset
- Script artifact
- Voice synthesis
- Format profile
- Publish slot
Observed in production
- 95 %Production acceleration
- Multi-plateformeOutput by default
05 · Platform · Multi-tenant
Multi-Tenant AI Content Platform
Multi-tenant SaaS platform where each client organisation gets its own space, brand voice and governance rules — on a common API-first architecture.
Role · Architect · Build
- Context
- Need to serve multiple clients with distinct editorial, legal and operational requirements, without duplicating the stack.
- Design
- Strict tenant isolation, granular capabilities, versioned configurations, common observability. Agency tenants vs client sub-accounts distinguished structurally.
- Governance
- No data crossover between tenants. Each capability change is tracked. Exports respect tenant scope.
- Outcome
- Fast onboarding of new tenants. Single maintenance for the common stack. Customisation without dedicated code branches.
Primitives
- Tenant scope
- Capability ACL
- Versioned config
- Tenant observability
06 · CRM · Lifecycle
Google Reviews & Customer Re-activation
Google Reviews solicitation and client re-activation system articulated on the existing CRM base. Per-segment personalisation, follow-up governance, consent respected.
Role · Architect · Build
- Context
- Rich but underused CRM base. Insufficient Google reviews for local SEO. Manual, inconsistent reactivation.
- Design
- Behavioural segmentation, typed solicitation scenarios, respectful cadence, tracked opt-out, measurement of effect on rating and repeat.
- Governance
- No solicitation without clear legal basis. Capped cadence. Instant opt-out, traced and respected across all channels.
- Outcome
- Strengthened local reputation. Predictable reactivation of dormant clients. Measurable effect on rating and repeat business.
Primitives
- Segment scope
- Solicitation contract
- Consent ledger
- Repeat signal
07 · Multi-Agent · Unified
Unified Multi-Agent Voice + Chat Ops
Unified voice + chat front where agents share common memory, common rules and coherent escalations — whatever the inbound channel.
Role · Architect · Build · Run
- Context
- Voice and chat channels operated in silos, fragmented client memory, inconsistent experiences depending on channel, escalations that restart from zero on each reply.
- Design
- Unified client session layer, stable cross-channel identifiers, shared business rules, common voice+chat observability. Channel-specific agents attach to this layer without duplicating it.
- Governance
- Single decision grid, single operational memory, single escalation policy. Channel divergences are configurations, not forks.
- Outcome
- Client is recognised from one channel to the next. Human operators arrive in the escalation with full history. Business rules change in a single place.
Primitives
- Client session
- Cross-channel identity
- Shared rule store
- Channel adapter
- Unified trace
From proof to case
A system analysis only matters when it becomes operable.
For a concrete AI subject, the guided Reveal journey qualifies context before any meeting. Classic contact stays available for direct exchanges.