Features
Agentic Foundry provides comprehensive capabilities for building and managing intelligent agents with minimal coding effort.
Features Overview
Feature Descriptions
Core Development Features
1. Low-Code Agent Creation
Reduced development time with a low-code approach. Simply provide the tool logic and workflow definitions — the framework automatically handles the rest.
2. Agent & Tool Management
Seamlessly onboard, update, or remove agents and tools through an intuitive interface.
3. In-Platform Customization
Customize tools, workflows, and control logic directly within the framework — no external IDEs or redeployment required.
4. Reusable Components
Design agents and tools as modular, self-contained components. These can be reused across multiple workflows and projects, promoting consistency and accelerating development.
Workflow & Control Features
5. Human-in-the-Loop
Integrate manual checkpoints into automated workflows to enable human review and intervention during critical decision-making steps. Maintain oversight and control where it matters most, especially in sensitive or high-risk operations.
6. Agent Workflows
Visually design multi-agent workflows using a drag-and-drop canvas. Connect multiple agents in sequence or parallel with conditional branching, input/output management, and reusable workflow configurations.
7. Orchestrator Agent
Manage complex multi-agent systems through a central coordinating agent. The Orchestrator assigns tasks, handles inter-agent communication, resolves conflicts, and ensures that distributed agents work toward a unified goal.
8. Tool Interrupt
Review, modify, and approve tool executions before they are processed. This interactive mode provides step-by-step control over tool calls, allowing parameter editing and approval at each stage.
9. Dynamic Workflow Automation
Automate workflows that can adapt to real-time inputs, system feedback, or environmental changes. The platform supports dynamic branching, state-aware execution, and conditional task handling.
10. SSE Streaming
Server-Sent Events stream each step of the agent's internal processing to the UI in real-time — providing complete visibility into tool calls, reasoning steps, and execution progress as they happen.
11. Canvas Screen
An advanced visualization feature in chat inference that automatically renders structured data as tables, charts, graphs, or images — providing rich, context-aware output directly in the chat interface.
12. Prompt Optimization
An automated system that generates, tests, and evolves multiple prompt versions using Pareto sampling and LLM-as-judge scoring to find the most accurate and efficient system prompt for your agent.
13. Validators
Create custom validation logic — similar to tools — and map them to agents during onboarding. When enabled via the Validator toggle in chat, validators automatically check agent responses against expected patterns, returning a validation score, status, and feedback. Supported across React, React Critic, Planner Executor, Meta, and Planner Meta templates. Validators help ensure response accuracy, format compliance, and overall quality in real time.
Intelligence & Learning Features
14. Semantic Memory
Persistent cross-session memory that stores and retrieves user-provided facts, preferences, and contextual information using Redis and PostgreSQL — enabling personalized, context-aware interactions.
15. Episodic Memory
Agents learn from past conversational experiences by storing query-response examples. Using bi-encoder and cross-encoder similarity scoring, agents apply few-shot learning to improve future responses based on positive and negative feedback.
16. Custom Knowledge Bases
Equip agents with domain-specific intelligence by uploading documents (PDF, TXT) as knowledge bases. Agents reference these during inference for accurate, context-aware answers.
17. Feedback-Driven Learning
Continuously improve agent performance through direct user feedback. The system supports structured and unstructured feedback loops that fine-tune decision-making, language understanding, and tool usage over time.
Data & Integrations
18. Data Connectors
Connect to and interact with PostgreSQL, SQLite, MySQL, and MongoDB databases directly from agent workflows through a simple connection interface.
19. MCP Registry
Model Context Protocol integration for connecting agents to external tools and services. Supports local, remote, and file-based MCP servers with real-time tool discovery, enterprise security, and audit logging.
20. Flexible Model Support
Plug in different LLMs or SLMs as per task needs. Supports Azure OpenAI, OpenAI, and custom LLM providers through a straightforward configuration process.
21. Model Server Integration
Centralized hosting of bi-encoder and cross-encoder models via a FastAPI-based model server — eliminating redundant downloads and local storage across multiple environments.
Security & Access
22. Role-Based Access Control (RBAC)
Three distinct user roles — Admin, Developer, and User — each with specific access levels and permissions across the platform including tools, agents, vault, data connectors, and inference features.
23. JWT Authentication
Secure API endpoint authentication using JSON Web Tokens. All API requests are authorized via Bearer tokens, ensuring integrity and controlled access to platform endpoints.
24. Vault (Secrets Management)
A secure storage system for API keys, URLs, and credentials with private (user-only) and public (organization-wide) vaults. Tools retrieve secrets by reference — no hardcoded values required.
25. Agent Export
Export complete agent configurations including tools, dependencies, static files, validators, and SSE configurations as a self-contained package for backup, migration, or redeployment across environments.
Evaluation & Monitoring
26. LLM-Based Evaluation
Assess agent quality using the LLM-as-a-judge methodology. Compare two models side-by-side across predefined metrics with real-time progress updates via SSE.
27. GroundTruth Evaluation
Measure agent performance by comparing generated responses against expected outputs provided via CSV or XLSX upload. Produces comprehensive accuracy and quality metrics.
28. Consistency & Robustness Evaluation
Evaluate response stability across identical queries over time (consistency) and resilience against edge cases, malformed inputs, and adversarial scenarios (robustness).
29. Telemetry & Monitoring
Monitor system performance and behavior using OpenTelemetry for distributed tracing, Arize Phoenix for model observability, and Grafana for real-time dashboards and alerts.