Introduction
Agentic AI systems: concise summary of practical practices from Shayan Taslim to build reliable production agents.
Agentic AI systems orchestrate primary agents and stateless subagents to automate analysis and workflows with predictability and scale
Context
Shayan Taslim applied agents to process feedback, generate changelogs and scale user analysis. Complex hierarchies failed; simplicity and observability won.
Two-tier model
A primary agent maintains context; subagents are stateless functions focused on single tasks
Why it works: improves visibility, debugging and enables safe parallel execution
Core principles
- Stateless by default: subagents without memory ensure deterministic outputs
- Structured communication: clear objective, bounded context, output schema and constraints
- Task decomposition: combine sequential pipelines and map-reduce for efficiency
- Fail-fast and graceful degradation: try alternatives and return useful partial results
- Monitoring and metrics: task success rate, response quality, latency and error patterns
Orchestration patterns
Practical patterns: sequential pipeline, map-reduce, consensus; avoid deep hierarchies
- MapReduce for large-scale analysis (e.g., hundreds of feedback items)
- Sequential for multi-step report generation
- Consensus for critical decisions with voting/merge
Context management and limits
Provide minimal context: full isolation or filtered context beats long message windows
Methods: explicit summaries, structured context, or reference passing for documents
Performance and optimization
Match models to tasks, use batching, cache by prompt hash and controlled parallelism to reduce cost and latency
Error handling
Implement retries (network, rephrasing, alternative model), exponential backoff and return partial results with suggested actions
Conclusion
Summary: keep agents small and focused, avoid state in subagents, instrument and scale with parallelism and caching; these patterns enabled UserJot's beta in production
Source: Shayan Taslim at https://userjot.com/
FAQ
What is the minimum viable agentic AI system?
One primary agent that holds context and one subagent that performs the task; expand only as needed
Should subagents be stateless?
Yes. Stateless subagents provide determinism and simplify testing and caching
How to handle rate limits with parallel agents?
Use a token-bucket rate limiter to launch agents up to the quota and queue the rest
Which models suit primary agents vs subagents?
Primary agents should use stronger reasoning models; subagents can use fast, task-focused models
How to test agentic AI systems?
Unit-test subagents with fixed inputs, mock orchestration for integration tests, and run controlled end-to-end tests
How much context should subagents receive?
Minimal context is best: prefer explicit summaries or structured context when needed