AI Agents: Frequent Failures and the Syniaps Method

Projects involving agents halted within weeks, frozen sequences that break, and poorly framed validations: Syniaps outlines the causes of failure it observes and the recipe it applies. The publisher emphasizes a corporate memory, agents by profession, and human validation, with hosting in France and contractual integrations with Claude or ChatGPT.

Validate Before Sending: A Rule Set by Syniaps

Syniaps asserts that every binding action—follow-ups, quotes, accounting entries—awaits human approval. The publisher links part of the observed failures to the absence of clear rules: either everything gets stuck and the agent remains in draft mode, or everything is sent out, resulting in clumsy messages reaching major clients. In its usage framework, only classification, preparation, and bank reconciliation are executed without human validation.

Three Causes of Early Project Cessation for Agents

According to Syniaps, many agent projects stop after just a few weeks due to recurring reasons that are independent of the models' power. The first cause: the agent does not know the company and becomes ineffective on issues like customer credit, contractual commitments, or the status of a follow-up, leading teams to endlessly re-explain. The second cause: predefined sequences fail as soon as an unexpected event occurs, forcing manual intervention and adding to the workload. Syniaps argues that an agent that chooses its path based on what it knows navigates these situations better.

An Architecture Focused on Memory and Path Choice

The publisher advocates for a contextualized agent, equipped with a memory fed by the company's sources such as messaging, storage, calendars, CRM, or accounting. Syniaps claims to connect heterogeneous facts—contract and client, email and deadline, quote and follow-up—through "synapses," creating an organized memory accessible to the entire team. It states that it has built its solution around three pillars derived from observed pitfalls: corporate memory, an agent that decides its path, and human validation of binding actions, always starting by clarifying what the company knows and who decides.

Claimed Professional Uses and Announced Timelines

Syniaps offers agents designed for various professions, including sales, human resources, marketing, administration, and finance. For a B2B client, the sales agent qualifies leads, personalizes and sends follow-ups up to D+3, and enriches the pipeline without requiring CRM access, with tracking being automated. In the case of an artisanal soap factory, the marketing agent handles the writing and updating of product sheets, including translations, a task previously performed weekly by a team. For the HR function, a customized tool has been developed without developer intervention to manage personnel files and payroll deadlines, without altering the existing organization. At several clients, the administrative agent processes expense report classification, issues and sends monthly invoices, and prepares bank reconciliations, these operations being classified as repetitive and time-consuming. The publisher promises responses in seconds in French with source citations, usage in professional language, installation in fifteen minutes, operation by mission with result validation, without re-entry, manual classification, or hiring developers.

Hosting in France and Contractual Integrations

Syniaps indicates that hosting and storage are located in France on a dedicated client server. It specifies that no data is used to train a public model. Only the strictly necessary information for the operation of the selected model, Claude or ChatGPT, is transmitted to the provider under a contract. According to the publisher, this configuration enables the agent to connect to valuable company information.

Positioning Against SaaS Stacks and Usage Promise

Syniaps describes SaaS stacks that pile up, incur costs, and silo data, with teams reduced to clicking tasks. It advocates for intelligence that covers the entire company rather than a stack of tools based on needs, arguing that the agent handles data entry while teams focus on their specific tasks. The publisher suggests calculating a rough estimate of costs by adding subscriptions per user. In this context, it distinguishes three often-confused objects: a GPT wrapper, a memory-less interface that starts from scratch; a no-code tool, a sequence conditioned to a diagram and subject to stops outside of expected cases; and a contextualized agent, equipped with memory and capable of deciding its path.