Cognion: Zero-Code Agent Management
A highly modular and scalable platform to create, configure, extend, and connect agents
Highly modular and flexible, Cognion pulls together data that define an organization's uniqueness and purpose. Each of its "cells" is a self-contained component with API-integration into structured and unstructured databases. This enables agents (the "neurons") powered by foundational and custom LLMs to flexibly and organically digest and synthesize vast quantities of information and data, providing visibility and consistency when interacting with it. Like neurons, any agent can have, in principle, access to everything else going on inside the organization, and within its larger ecosystem.
By dynamically assembling, exposing and sharing knowledge and perspectives that consider the general and specific background of the organization, users can extract highly relevant aspects and perspectives, and apply such knowledge where and when it matters most. This then enhances quality of thinking and decision making - across an entity's units and even in collaboration with other organizations.
Agent categories
Self-contained components with API
RBAC - role-based access control
Flexible frontend modification and enhancement
3-level customization of logic and behavior
Full API support for all data interactions
Object-level authentication for agents and instances
Agent orchestration engine for logic and behavior
Agent personalization through agent instances
Postgres data storage
Encryption support
Agent-to-Agent (A2A) chaingin and interaction'
Agent run monitoring of work output
Vector data storage
Strict tenant-separation by organization and user
Cockpit with detailed logs of instance runs
The agentic future involves autonomous and semi-autonomous actions of agents in self-organizing networks. To deliver relevant output, agents must be purposefully directed.
Delivering the most relevant input can be seen as a three-step process. it reflects three “levels” of knowledge abstraction and distillation, which often overlap in real life.
3 Layers of Customization
The platform contains three integrated layers of configuration
Organization Settings
Setting up a new organization can be simple as entering its name, optionally uploading a logo or avatar, and setting up an email account and AI model API parameters. It then can be customized via entries in half a dozen of fields describing its corporate context, and organization-level prompts.
This establishes the general background for agents and starts making them broadly aligned with the organization’s mission, positioning, products, culture, markets, and general objectives. At that moment, several pre-packaged agents are available for immediate use.
Most of these parameters can be changed to reflect a continuous fine-tuning of agent performance. Others will be updated by the agents themselves, as they learn from interaction and contextual data collected.
User Settings
Personal, user-specific settings can be either very simple, or highly granular. Users enter their name, email, and optionally an avatar, and user-specific parameters roughly equivalent to the generally entered ones from the corporate level. They may also add parameters for personal emails so agents can send emails on their behalf, or access their email accounts for analyses and actions, or personal data storage locations and rules, and the like.
Beyond such settings, users can enter their unique personal context and prompts to personalize agent actions and output. These typically define the personal background and preferences, objectives, and communication style unique to the user. Multiple versions of each give further flexibility. They enable the setting up of different agents or agent "instances" that consider varying context or parameters.
Agent Settings
Agents are the sets of specific capabilities that involve at least one AI model, input data, logical rules, and particular capabilities to process input and generate output. Most of the agents’ parameters and input settings are derived from organizational, user, or agent tabs. Agent-specific settings can provide additional context that is unique to an agent’s purpose.
Agents are governed by (a) their categories and (b) their specific setup steps and logic as defined in the Agent “Orchestra”. This Orchestra can define multi-step processes.
Cognion's agent categories enable the defaulting of options of settings to those that typically apply in a certain context. On the other end, agent instances facilitate more detailed and unique settings below the agent level, while keeping the agent's general logic and capabilities.
Interactive agents have a special power: They are not limited to hard-programmed logic or pre-established interfaces – and still they can access more data near-instantaneously than any other tool in human history while adjusting to shifting context and requirements.
An agent advisor can prioritize the specific purpose and preferences given via prompts and contextual information without being limited by those. There is no hardcoding of knowledge. Rather, the particular advantages of these agents come from the opposite. They can find patterns and relations that we cannot program because humans are not aware of them in the first place. Usually, these are hidden among the data’s overwhelming volume or complexity. The agents’ strength is their ability to near-instantaneously find non-obvious connections between dots that humans are unable to identify or may have missed because the links are non-intuitive.
Adding Components
Since all configuration components, the tabs, are self-contained and with their own data storage and API, a theoretically unlimited number of additional tabs can be added without directly affecting existing ones. Examples are hooks into devices or sensors, external APIs, internal data sources, third party software, and external data sources and tools of any kind.
Hooks to satellite feeds, industrial robots, self-driving cars, health or fitness wearables, or other data and sensors can be defined in a standard format. This makes them available for agents to act on. By combining such and other components, there is no practical limit on what agents can perform as an extension of an organization or person.
Agent Orchestration
Cognion’s orchestra defines agent’s logic, parameters, tools used, data input and output, and how to process and prioritize context, prompts, instructions. Modifying these effects agent behavior. Cognion does not custom-code instructions or hardcode mappings or even detailed logic.
The use of pegs and tabs makes the treatment of data generic, by their type. This simplifies and streamlines the orchestration process. It turns hardcoding into data-driven 'configuration'. Inherent modularity let's one integrate new components on top of existing ones.
Agent-to-Agent (A2A) Connections
Cognion can have agents interact directly. They can talk to each other via voice in meetings or back-and-forth text prompts and outputs. This way, they may consult each other, up-and-down vote on joint endeavors, or give feedback on a variety of matters relevant to one or both. Such interactions are technically scalable without practical limits. It is an automated way to access an improved and automated version of the “wisdom of the crowds”.
This can be within or across organizations. Agentic chains and communities can pursue their own optimizing purposes. They may identify issues and share relevant data, sentiments, and information, autonomously. Agents can debate among themselves and involve human decision makers afterwards.
Custom-Trained Cognitive Twins and SLMs
Cognion agents deployed within an organization evolve ever larger amounts of strategic, tactical, and potentially also operational, knowledge of this entity and its ecosystem. Together, this turns them into the rough equivalent of a Corporate Cognitive Twin.
Such evolving knowledge can be integrated into the weights of AI models themselves. By fine-tuning open-source AI models, they reflect essential “intelligence” of the organization inherently, even without specific prompts, context, or access to foundational models or external data. Continuous updates can “bake” the essence of an entity in its own AI model.