Agents: Core Platform
Evonic is fundamentally an agentic AI platform. Agents are the core building blocks that power everything — from simple chatbots to complex multi-agent workflows. With Evonic, you design agents from concept to production, defining every aspect of their behavior: the underlying model, available tools, knowledge base, communication channels, and installed skills.
What is an Agent?
Section titled “What is an Agent?”An agent is an independently configured, LLM-powered assistant that can:
- Reason and plan — Break down complex tasks into actionable steps
- Take actions — Execute tools, run code, query databases, and call APIs
- Remember context — Maintain conversation history across sessions with persistent state management
- Communicate — Talk to other agents natively via the agent-to-agent protocol
- Connect anywhere — Deploy to Telegram, WhatsApp, Discord, or web interfaces
- Learn from knowledge — Access reference documents and custom knowledge bases
- Operate anywhere — Run on local machines, remote SSH servers, or tunnel devices via Workplaces
Anatomy of an Agent
Section titled “Anatomy of an Agent”When you design an agent in Evonic, you define these components:
| Component | Description |
|---|---|
| Model | The LLM that powers the agent’s reasoning — OpenAI-compatible, local, or cloud |
| System Prompt | The agent’s personality, role, and behavioral guidelines |
| Tools | Executable capabilities (bash, runpy, web search, custom API tools) |
| Knowledge Base | Reference files the agent can read during conversations |
| Artifacts | Persistent file output system for saving work results across sessions |
| Channels | Messaging platforms the agent connects through (Telegram, WhatsApp, etc.) |
| Skills | Installable packages that bundle tool definitions with Python backends |
| Workplace | Execution environment — local directory, SSH server, or Evonet-connected device |
Three Differentiating Concepts
Section titled “Three Differentiating Concepts”Evonic’s architecture is defined by three differentiating concepts — Workplaces, Agent-to-Agent Communication, and HMADS (Heuristic Mal-activity Detection System) — which are explained in detail on the Design Philosophy page.
Architecture
Section titled “Architecture”User Message ↓Channel (Telegram, WhatsApp, Web, etc.) ↓Agent Runtime ├─ Load agent config (system prompt, model, tools, skills) ├─ Load/create session (per-user persistence) ├─ Build messages (system prompt + history + new message) ├─ Call LLM ├─ Execute tool calls (if any) ├─ HMADS check on each action ├─ Loop until final response └─ Save messages to session ↓Response → Channel → UserKey Concepts
Section titled “Key Concepts”Agents
Section titled “Agents”Each agent has a workspace directory (agents/<agent_id>/workspace/) for file operations. This is configurable per-agent and provides an isolated filesystem context for tools like runpy, bash, and write_file.
An agent is defined by a slug ID (e.g., bookstore_bot, library_assistant), a system prompt, an optional model override, a workplace assignment, and a set of assigned tools and skills. Manage agents at /agents in the web UI.
Knowledge Base
Section titled “Knowledge Base”Each agent has a filesystem directory (agents/<agent_id>/kb/) for storing .md reference files. The agent can read these files using the built-in read tool during conversations. This keeps knowledge files editable and version-controllable.
Artifacts
Section titled “Artifacts”Each agent has its own artifacts directory (agents/<agent_id>/artifacts/) for persisting work outputs across sessions. Agents use the save_artifact tool to store reports, images, or any generated files. Artifacts survive session deletion and are accessible through the web UI. See Artifacts for details.
Agents can use tools from the tool registry. In production mode, tools call real Python backend implementations (in backend/tools/). The built-in read tool is always available and scoped to the agent’s KB directory.
Agent State
Section titled “Agent State”Agents operate in one of two modes: Plan or Execute. The agent state is persisted across conversation turns and survives LLM context summarization. Agents always start in Plan mode on a new session. See Agent State for details.
Channels
Section titled “Channels”Channels connect agents to external messaging platforms. Each channel type (Telegram, WhatsApp, etc.) implements a common interface. Multiple channels can be connected to the same agent. See Channels for details.
Sessions
Section titled “Sessions”Conversations are persisted per-user, per-agent, per-channel. The same user can have separate conversations with different agents. Session history is used as context for each LLM call.
Agent Quick Search (Ctrl+G)
Section titled “Agent Quick Search (Ctrl+G)”Introduced in v0.7.0.
Press Ctrl+G anywhere in the web UI to open a keyboard-driven overlay for instant agent search and navigation. Type a partial name to filter agents, then jump directly to any agent without touching the mouse or leaving the current page.
Slash Commands
Section titled “Slash Commands”Agents support slash commands for quick actions. These work in both the web chat and channel interfaces:
| Command | Description |
|---|---|
/clear | Clear the current conversation history |
/clear-memory | Clear the agent’s long-term memories |
/help | Display available slash commands |
/summary | Trigger conversation summarization |
/exec | Switch from plan mode to execute mode |
/shutdown | Shut down the Evonic server (super agent only) |
/cwd | Show the current workspace directory |
/cd | Change the agent’s workspace directory |
See Slash Commands for full details.
Use Cases
Section titled “Use Cases”Evonic agents excel in a wide range of scenarios. Here are some of the most common:
| Category | Use Case | Description |
|---|---|---|
| Customer Service | Support Agent | Handle tickets, FAQs, refunds, and escalations |
| Personal | Personal Companion | Daily tasks, reminders, research assistance |
| Multi-Agent | Agentic Swarm | Collaborative agents working together |
| DevOps | Infrastructure Agent | Server monitoring, deployment automation |
| Data | Research & Analyst Agent | Data extraction, summarization, reporting |
| HR | Recruitment Agent | Candidate screening, interview scheduling |
| Education | Tutor Agent | Personalized learning, Q&A, assessments |
| Healthcare | Triage Agent | Symptom assessment and appointment scheduling |
| E-Commerce | Shopping Assistant | Recommendations, order tracking, returns |
| Finance | Financial Advisory | Portfolio analysis, market summaries |
| Enterprise | Agentic ERP | Supply chain, inventory, procurement, finance |
| ML/AI | AI Workflow Orchestration | End-to-end ML pipeline automation |
For detailed walkthroughs of each use case, see the Use Cases page.
Quick Start
Section titled “Quick Start”- Navigate to
/agentsin the web UI - Click + New Agent and enter an ID (e.g.,
my_assistant) - Configure the system prompt in the General tab
- Select the model for the agent’s reasoning
- Upload knowledge files in the Knowledge tab
- Assign tools and skills in the Tools tab
- Test immediately using the Chat tab
- Optionally connect a Telegram or WhatsApp bot in the Channels tab