Agents & Orchestration
Patterns for composing multiple model calls, tools, and memory into workflows and autonomous agents.
- Agent MemoryPTL-0071
- Agentic WorkflowPTL-0064
- Evaluator-OptimizerPTL-0069
- Orchestrator-WorkersPTL-0068
- ParallelizationPTL-0067
- Prompt ChainingPTL-0065
- RoutingPTL-0066
- AI AgentPTL-0063
- Context EngineeringPTL-0072
- Meta-PromptingPTL-0070
Definitions
- Agent Memory
- Agent memory is the set of mechanisms that let a language-model agent store information beyond a single context window, such as conversation summaries, retrievable records of past events, and reflections, and bring the relevant parts back into context later.
- Agentic Workflow
- An agentic workflow is a system in which language models and tools are orchestrated through predefined code paths, as opposed to an agent that chooses its own steps.
- AI Agent
- An AI agent is a system in which a language model dynamically directs its own process and tool use in a loop, deciding what actions to take based on environment feedback until a task is complete.
- Context Engineering
- Context engineering is the practice of curating the full set of tokens a model sees at each step, including instructions, tools, retrieved data, memory, and conversation history, to maximize the chance of the desired behavior within a limited attention budget.
- Evaluator-Optimizer
- Evaluator-optimizer is a workflow loop in which one model call generates a response and another evaluates it against criteria and provides feedback, repeating until the output passes.
- Meta-Prompting
- Meta-prompting uses a single model as a conductor that breaks a task down and writes prompts for fresh instances of itself acting as specialized experts, then integrates their outputs.
- Orchestrator-Workers
- Orchestrator-workers is a pattern in which a central model dynamically breaks a task into subtasks, delegates them to worker model calls or subagents, and synthesizes their results.
- Parallelization
- Parallelization is a workflow pattern that runs several model calls simultaneously and aggregates their outputs, either by splitting a task into independent sections or by running the same task several times and voting.
- Prompt Chaining
- Prompt chaining decomposes a task into a fixed sequence of model calls, where each call processes the output of the previous one, often with programmatic checks between steps.
- Routing
- Routing is a workflow pattern that classifies an incoming request and directs it to a specialized prompt, tool, or model suited to that category.