Show Notes
Today we break down the difference between Genic (genetic) workflows and AI agents as explained by Anthropic and OpenAI, plus practical guidance on when to use each and how to start building with simple patterns.
Genic Workflows vs AI Agents
- Genic (genetic) workflows: predefined sequences of steps with a bit of AI sprinkled in. Humans can be in the loop. The path is fixed and predictable.
- AI agents: LLMs that direct their own process and tool usage. More autonomous and open-ended, with humans in the loop only at milestones or checkpoints if needed.
- Core distinction: path and rigidity (genic) versus dynamic decision-making (agents).
How the patterns look in practice
- Genic workflow diagrams: start, 1, 2, 3, optional human review, then loop or proceed. If human review is required, the flow can redirect back into the chain.
- Agent loop diagrams: start, LLM chooses the next tool, observes results, uses context to decide if the goal is met, then repeats or finishes.
What Anthropic and OpenAI emphasize
- Anthropic: workflows are simple, composable patterns; agents are autonomous systems that can direct their own work and tool usage.
- OpenAI: agents are autonomous, capable of performing tasks independently; emphasis on when to use autonomy vs structured workflows.
- Practical takeaway: start with simple, composable patterns; only add autonomy when the task benefits from flexible decision-making at scale.
Patterns, building blocks, and guidelines
- Core building blocks:
- LM augmentation with augmentation like retrieval (vector embeddings, memory).
- Tools as function calls and memory to retain useful information.
- Focus areas:
- Tailor capabilities to your use case.
- Provide an easy, well-documented interface for the LM.
- Practical note: many patterns can be implemented with direct LM APIs in just a few lines of code; avoid over-engineering upfront.
Frameworks and tooling (quick landscape)
- Rivet (open source, TS-based visual workflow): graph-like tool calls; good for visual wiring of prompts and tools.
- Vellum (open source): TS-based, open tooling to explore agent-like workflows.
- LangChain: popular framework; OpenAI’s responses API and TS support are worth noting for TS-heavy projects.
- Bedrock AI Agent Framework (AWS): producer-focused agent framework for AWS environments.
- MCPs (modular tool patterns): simple client implementations to wire in capabilities; helps manage tool calls and guardrails.
- Takeaway: start with direct LM API work or lightweight patterns, then layer in open-source tools as needed to reduce boilerplate.
Cookbook patterns explained (with practical use cases)
- Prompt chaining: decompose a task into subtasks that the LM handles sequentially.
- Use case: create a YouTube video outline by breaking the topic into sections.
- Prompt routing: classify input and route to specialized follow-up tasks; enables separation of concerns.
- Use case: route general questions, technical questions, or marketing tasks to different prompts or models.
- Memory and retrieval augmentation: use embeddings to fetch relevant content or prior context; memory stores important results.
- Use case: reference prior matches or build a context-aware assistant.
- Parallelization: run the same task on multiple models or configurations and aggregate results.
- Use case: QA across models to compare outputs or generate diverse options.
- Orchestrator pattern: a central LM delegates to worker LLMs and synthesizes results.
- Use case: scalable team-like workflow where a product owner coordinates engineers, reviewers, and QA agents.
AISDLC in practice (the workflow idea)
- AI Software Development Life Cycle: map Scrum-like steps to agent-driven tasks.
- Smart chunking: keep context small to improve accuracy; break work into independent chunks that can run in parallel.
- Routing-driven progress: use routing to determine which path an agent should take next (e.g., PRD review, coding, or review).
- Outcome: a scalable, agent-augmented development flow with clear handoffs between roles (engineering manager, engineers, reviewers).
Practical tips for getting started
- Start simple: use LM APIs directly and implement a few patterns (prompt chaining, basic routing) before adopting heavy frameworks.
- Beware abstraction layers: frameworks can obscure prompts and responses, making debugging harder.
- Lean into TypeScript if you’re TS-focused; many TS-compatible tools (and OpenAI’s TS support) make life easier.
- Use simple guardrails: MCPs and tool interfaces help prevent miscalls and keep flows predictable.
- For your own projects, try a small AISDLC setup: smart chunking, parallel builders, and a lightweight orchestrator.
Next steps and channel plan
- The next video explores concrete implementations for a YouTube assistant or a coding agent, plus deeper MCP exploration.
- Daily channel vs main channel: quick dives and hands-on builds on daily; deeper project builds on the main channel.
Links
- Anthropic: Building Effective Agents (official doc/blog)
- OpenAI Agents SDK (platform docs)
- LangChain.js (JavaScript/TypeScript)
- Rivet (open-source TS-based visual workflow)
- Vellum (AI workflow platform)
- Amazon Bedrock Agents
- Model Context Protocol (MCP) (pattern/tooling for integrating capabilities with LLMs)
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