Duration: 25:52
PART 1 — Analytical Summary 🚀
Context 💼
This session, led by members of Odoo’s R&D AI team, unveils how Odoo AI Agents work under the hood—what components they rely on, how they retrieve knowledge, and how they perform actions in apps. The goal is to help teams build more effective, trustworthy, and task‑oriented agents that are tightly integrated across Odoo. The talk covers architecture, best practices for prompt design, and a Q&A on deployment, security, and roadmap items.
Core Ideas & Innovations ⚙️
At the heart of Odoo’s approach are two pillars: knowledge and action. Agents combine a RAG (Retrieval-Augmented Generation) layer for precise, source‑grounded Q&A with a Tools & Topics execution framework for performing business operations.
On the knowledge side, agents ingest “sources” such as uploaded files, Documents app content, Knowledge articles (with child pages), and URLs. Indexation runs in the background via cron jobs, with smart optimizations: Odoo checks for duplicate assets via checksums to avoid re‑embedding, then chunks content for higher accuracy and lower resource use. Embeddings are generated per the selected LLM provider—e.g., OpenAI or Google Gemini—and switching models triggers automatic re‑embedding to keep results consistent. For URLs, Odoo’s crawler respects robots.txt and reports failures with retry options.
During retrieval, the user’s query is embedded and matched against the agent’s active sources only. The system returns the top‑ranked chunks (e.g., top 5), plus their source names for transparent citations. These are blended with the agent’s system prompt, the active Topic description, and the Tool schemas before calling the LLM—ensuring the model answers from context and knows how to act when appropriate.
On the action side, Tools are essentially AI‑enabled Server Actions with clear descriptions and parameter schemas (e.g., name, email, phone for “Create CRM Lead”). Topics group multiple tools with guidance on when to use them—like an “information retriever” topic that exposes read‑only database queries for questions such as sales volumes. Importantly, Odoo executes the server action itself (not the LLM), providing a security boundary: the AI proposes, Odoo performs.
To improve reliability, the team emphasizes system prompts: define a precise persona, state exact output requirements, provide examples, and use separators/placeholders for long prompts. Small prompt tweaks can have outsized effects, and different models respond differently—there’s no single “right” prompt, only clarity and precision.
Impact & Takeaways 🧠
The result is a cohesive agent framework that can both “know” and “do” across Odoo—answering questions with citations and performing tasks safely. Operations teams can spin up unlimited, purpose‑built agents to streamline CRM intake, support, analytics Q&A, and more. The “restrict to sources” control boosts trust by confining answers to provided documents, and source referencing aids auditability.
Practical considerations emerged in Q&A: - Model support: current integrations cover OpenAI and Google Gemini; no fine‑tuning and no LLM caching today. Choosing the “best” model is case‑by‑case. - Deployment: on Odoo SaaS, usage is included (use responsibly); on Odoo.sh or on‑premise, bring your own API keys. Agents can run on‑prem with your credentials. - Data and security: database queries respect user access rights. However, any user can use any agent today; there’s no per‑agent audience restriction yet, so avoid loading sensitive sources until group controls land. For live chat, website snippets can hook agents; agents can propose answers but won’t auto‑send messages on behalf of users. - Roadmap notes: CSV as a first‑class RAG source is planned (with column‑aware chunking); folder auto‑sync from Documents isn’t available yet.
Altogether, Odoo AI Agents bring integrated, explainable automation to business workflows—reducing manual steps, improving response quality, and ensuring actions happen with guardrails and references. 💬
PART 2 — Viewpoint: Odoo Perspective
Disclaimer: AI-generated creative perspective inspired by Odoo's vision.
What matters to me is simplicity at scale. Agents should feel native—reading your knowledge, respecting your rules, and acting through the same server actions you trust every day. We’re not asking users to learn “AI”; we’re letting AI learn their business, one source and one tool at a time.
Integration is our superpower. Topics, tools, documents, and security policies are already in Odoo—AI just orchestrates them. The community will push this further: better prompts, reusable topics, and domain‑specific sources. We’ll keep refining the guardrails so teams can innovate confidently, without friction.
PART 3 — Viewpoint: Competitors (SAP / Microsoft / Others)
Disclaimer: AI-generated fictional commentary. Not an official corporate statement.
Odoo’s agent model is elegant in how it fuses RAG with tool execution via server actions. The UX is compelling and pragmatic for mid‑market teams: clear source control, citations, and a coherent way to act on data. The approach should accelerate adoption for CRM, support, and lightweight analytics.
Enterprise buyers will still probe scalability and governance: per‑agent audience controls, audit trails for tool calls, data residency, and regulatory compliance at scale (multi‑company, multi‑region). Model dependency (OpenAI/Gemini), lack of fine‑tuning, and no LLM caching may matter for cost/performance. The differentiation will be in UX and policy depth versus offerings like Microsoft Copilot Studio or SAP Joule—where integration with enterprise security, compliance, and observability is paramount.
PART 4 — Blog Footer Disclaimer
Disclaimer: This article contains AI-generated summaries and fictionalized commentaries for illustrative purposes. Viewpoints labeled as "Odoo Perspective" or "Competitors" are simulated and do not represent any real statements or positions. All product names and trademarks belong to their respective owners.