Google's Agent Development Kit (ADK) was an early framework for building AI agents. These posts document the ADK-to-Microsoft Agent Framework migration journey, comparing orchestration models, state management approaches, tool governance, and the architectural lessons learned from running both frameworks in production.
8 posts tagged with adk. ← All posts
The patterns that worked, the traps we fell into, and what we'd do differently.
What worked, what was hard, and what we'd do differently. Real numbers: 18 agents, 90 days, 5 governance policies, 4 provider swaps.
Running agents on Cloud Run, exposing via A2A, and wiring into production systems.
Deploy Microsoft Agent Framework multi-agent systems on Cloud Run with A2A agent-to-agent communication, autoscaling, load balancing, and production observability dashboards.
How to instrument agents for observability, error handling, and audit logging.
Migrate ADK callbacks to Microsoft Agent Framework composable middleware: decorators for audit logging, retry with backoff, token budget enforcement, and OpenTelemetry tracing.
How to port ADK's model hard-codes to Microsoft Agent Framework's provider factory pattern.
Zero-code LLM provider swaps across environments: Ollama for dev, OpenAI for staging, Azure Foundry for prod. Same agents, different models.
How to port tools, add policy enforcement, and integrate OPA.
Migrate ADK functions to Microsoft Agent Framework governed tools with policy enforcement, DLP scanning, approval gates, and OPA integration for production agent systems.
How conversation threads replace session state; how to track token usage across agent chains.
Sessions to threads: porting multi-turn state from ADK to Microsoft Agent Framework. Token budgeting, long-term memory, and conversation audit trails.
How to port ADK's orchestration callbacks to Microsoft Agent Framework builders without losing control.
How to port ADK's orchestration callbacks to Microsoft Agent Framework builders without losing control. The executor pattern: you own the loop.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months.
The philosophy, trade-offs, and what we learned converting 18+ agents in 3 months. Provider abstraction as the foundation for portable agents.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.