Your business workflows turned into tested AI agent plugins
Skills, hooks and MCP packaged with the agentic architect methodology. Your teams use AI as a reliable business tool, not a playground prototype.
Why ad-hoc LLM use does not survive production
No business guardrails
Models reply out of scope, without validation, without audit. Result: inconsistent answers, quality drift, legal exposure.
No system access
Without MCP, AI ignores your CRM, databases and internal tools. Teams copy-paste. Context stays out of the model.
No tests
No guarantee that a model update or prompt change will not break today’s workflow. Regressions are discovered in production.
No methodology
Impossible to maintain, hand off or evolve. The project lives or dies with whoever coded it. No skill transfer possible.
A Colombani.ai plugin packages six layers
Skills
Business expertise encoded as reusable, versioned modules: sales processes, accounting standards, clinical protocols, legal rules.
Hooks
Business rules applied automatically at each step: input validation, structured logging, guardrails, audit traceability.
MCP
Controlled access to internal systems (CRM, ERP, document base, business APIs) without exposing credentials to user or model.
Anthropic templates
Compliance with official best practices: prompt structure, tool use, agent loops, context management. Alignment guaranteed.
Tests + benchmarks
Reproducible evaluation suite. Comparison vs baseline (human process or competing tool). Metrics: accuracy, latency, cost.
Docs + monitoring
Technical docs for your teams. Usage dashboards. Regression alerts. You see drift before it becomes visible.
Agentic Architect in 5 phases
1. Architecture
Workflow mapping with the business team. Decision points and success metrics identified. Deliverable: functional spec + KPIs.
2. Design
Selection of required skills, hooks and MCP. Choice of suitable Anthropic templates. Effort and risk estimation. Deliverable: technical design.
3. Build
Continuously tested code. Strict compliance with Anthropic templates. Code review at every step. Deliverable: working plugin + test suite.
4. Benchmark
Evaluation on real cases vs baseline (human or existing tool). Metrics: accuracy, time, cost. Iteration until the defined quality threshold. Deliverable: benchmark report.
5. Deployment
Pilot rollout on one team. User training. Monitoring set up. Deliverable: plugin in production + documentation + 30 days of support.
Sample plugins designed with this methodology
Sales Ops Plugin
Inbound lead qualification: email reading, scoring, pre-meeting brief, CRM update. Stack: CRM-aware skills + validation hooks + HubSpot/Salesforce MCP.
Legal Review Plugin
Contract analysis: sensitive clause extraction, GDPR/AI Act check, risk report. Stack: legal skills + source citation hooks + document base MCP.
Month-end Close Plugin
Month-end reconciliation: multi-source aggregation, anomaly detection, report generation. Stack: accounting standards skills + control hooks + ERP MCP.
Medical Coding Plugin
ICD-10 code generation from clinical notes: extraction, suggestion, validation. Stack: medical nomenclature skills + traceability hooks + EHR MCP.
Six guarantees that make the difference
CCA Foundations from Anthropic
The only agentic architect certification recognized by Anthropic. Guaranteed alignment with official best practices.
Qualiopi
Quality framework transposed to engineering processes. Traceable deliverables, decisions and evaluations.
Tests + benchmarks
Every plugin ships with its evaluation suite. No "it worked when I tested it." Quality is measurable and reproducible.
Source code delivered
Code, configs, docs and repo belong to you. No SaaS, no lock-in, no recurring license on the plugin.
Monitoring included
Usage dashboards and regression alerts. You know if the plugin drifts before users report it.
Optional maintenance
Monthly subscription for evolutions and Anthropic model watch. You stay in control of the roadmap, no abandonment after delivery.
Frequently asked questions
What is the difference vs a classic AI development? +
A classic AI development delivers a script or API. A Colombani.ai plugin delivers a structured system (skills + hooks + MCP), tested, documented, compliant with Anthropic templates, with its evaluation suite and monitoring. A script dies on the first model update. A plugin evolves.
How long until a plugin is operational? +
From 4 to 12 weeks depending on workflow complexity and number of systems to integrate. A simple use case (lead qualification) can ship in 4 weeks. A multi-system plugin with complex integrations: 8 to 12 weeks.
Which systems can be integrated through MCP? +
Any system with an API (REST, GraphQL, SOAP). Connectors available for Salesforce, HubSpot, Notion, Google Workspace, Microsoft 365, GitHub, Slack and SQL or NoSQL databases. Custom connectors possible for internal systems.
What happens when Claude evolves? +
The plugin is versioned. Every Anthropic model update is tested against the evaluation suite before promotion. If maintenance is subscribed, Colombani.ai handles model watch and migrations. Otherwise, documentation explains how to do it in-house.
Can we use a local model for sensitive data? +
Yes. Plugins are compatible with Claude (cloud) and with local models (Mistral, Qwen via Ollama). The choice is made at scoping based on data sensitivity. See the sovereign AI consulting offer for full-local architectures.
How do you ensure the plugin stays reliable over time? +
Three mechanisms: the test suite runs on every change, monitoring alerts on usage or quality drift, documentation lets your team maintain the plugin without external dependency.
Which workflow do you want to turn into a plugin?
30 minutes of free scoping. You leave with a mini technical design, effort estimate and a clear view of risks.
Direct conversation with the CCA Foundations certified founder.