News

Agentic AI Systems: practical best practices for production

Article Highlights:
  • Two-tier design: primary agent and stateless subagents
  • Subagents as pure functions with no memory
  • Structured communication with clear objectives and constraints
  • MapReduce for large-scale feedback analysis
  • Sequential pipelines for multi-step processes
  • Consensus pattern for critical decision making
  • Cache by prompt-hash and use batching for efficiency
  • Monitor task success, quality, latency and error patterns
  • Use retries and graceful degradation for frequent failures
  • Minimize context to improve predictability
Agentic AI Systems: practical best practices for production

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

Introduction Agentic AI systems: concise summary of practical practices from Shayan Taslim to build reliable production agents. Agentic AI systems [...] Evol Magazine