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Sri Aradhyula

Sri Aradhyula

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What's New in 0.5 — Workflows, Scheduled Agents, BYO Credentials, Custom Agents, Fine-Grained Authorization, and Webex

· 10 min read
Sri Aradhyula
Sri Aradhyula

The 0.5 series is the biggest leap since the initial release. It ships a new workflow engine, agent-driven scheduled jobs, bring-your-own credentials for MCP tools, fully custom agent management from the UI, relationship-based access control across every resource, and a first-class Webex bot — alongside a dramatically improved admin experience. This post covers all of it.

AI Agent vs MCP Server

· 3 min read
Sri Aradhyula
Sri Aradhyula

Introduction​

Agentic Systems landscape is evolving rapidly, understanding the distinction between AI Agents and MCP Servers is crucial for building scalable agentic systems. While MCP Servers provide a standardized interface for tools and data sources, AI Agents leverage these capabilities to perform complex reasoning, planning, and execution tasks.

MCP​

MCP (Model Context Protocol) provides a standardized interface for LLMs to access tools and data sources.

Agent​

  • Agents are systems that use LLM-based reasoning to plan and take actions—including invoking MCP tools when needed.
  • Agents can handle complex tasks that could require multiple MCP tools and are capable of maintaining both short-term and long-term memory.
  • Agents encapsulate more than simple tool calls to MCP servers, providing an additional abstraction layer.

Agent Composition & Capabilities​

  • Tool Pruning: Optimizes the toolset for efficiency and relevance, including filtering tools from large MCP servers (with optional RAG for selection).
  • Long-term and short-term memory management: Agents utilize short-term memory for session-specific context and long-term memory for cross-session data recall.
  • Agent Registry: Manages agent versions and configurations.
  • Prompty Library: Provides a versioned repository for managing and evaluating prompts.
  • MCP Registry: Handles MCP server versions and configurations.
  • Maintain Conversation Context: Preserves conversation history within a thread for more effective LLM reasoning and action.
  • Prompt Engineering: Shapes agent behavior with well-designed system prompts.
  • Evaluation: Validates agent actions using standardized rubrics and tool trajectory audits.
  • Flexible LLM Bindings: Supports various LLM providers (e.g., GPT, Claude, Mistral).

This architecture makes agents composable, validated black-box units that can be reused across multi-agent systems and different personas.

Note: Agents built in CAIPE (Community AI Platform Engineering) project are exposed via the A2A protocol, standardizing external I/O and providing authentication and authorization support.

System Diagram​

Sequence Diagram​