Model Routing for AI Agents
Choose models by task requirements, policy, quality, latency, and cost instead of sending every step to one default.
Topic
#multi-agent
17 articles — updated daily
Scaling AI Agent Systems
Grow workload capacity safely by separating stateless runtimes from durable tasks and protecting constrained dependencies.
AI Agent Cost Optimization
Control the cost of successful agent outcomes, not merely the price of one model call.
Observability for AI Agents
A production observability model for agent, model, retrieval, tool, sub-agent, and infrastructure signals—with privacy and redaction controls.
MCP + A2A Production Architecture
A production architecture for combining MCP capability access with A2A specialist delegation while preserving policy, identity, tracing, and failure boundaries.
LLM Evaluation vs Agent Evaluation
LLM evaluation scores model outputs; agent evaluation measures the whole goal-directed system, including tools, state, constraints, reliability, latency, and cost.
Agent Traces and Trajectories
Learn how traces and trajectories represent observable agent execution without requiring storage or exposure of private chain-of-thought.
A2A Agent Discovery and Agent Cards
Learn how A2A clients discover remote agents, read Agent Cards, match skills and interfaces, evaluate suitability, and begin an interaction.
Shared State and Context in Multi-Agent Systems
Learn how multi-agent systems separate local context from shared workflow state, exchange artifacts, synchronize updates, persist checkpoints, and avoid state conflicts.
Sequential vs Parallel Agent Execution
Compare sequential, parallel, and hybrid agent execution by dependencies, latency, cost, state transfer, synchronization, aggregation, and failure handling.
Multi-Agent Coordination Patterns Explained
Learn seven practical multi-agent coordination patterns and how they manage roles, ownership, state, handoffs, aggregation, conflicts, and stopping.
Centralized vs Decentralized Multi-Agent Architectures
Compare centralized, decentralized, and hybrid multi-agent architectures across control, state, coordination, scale, observability, governance, and resilience.
Agent Routing Patterns: How Agents Choose the Next Worker
Learn seven agent-routing patterns, from deterministic rules and classifiers to semantic, capability-aware, hierarchical, and fallback routing.
Orchestrator vs Supervisor vs Router in Multi-Agent Systems
Compare orchestrators, supervisor agents, and routers by purpose, decision ownership, state responsibility, delegation, routing, and workflow control.
Agent Workflows and Orchestration Explained
Learn how agent workflows and orchestration coordinate steps, dependencies, branches, parallel work, retries, checkpoints, tools, agents, and humans.
Agent Handoffs, Delegation, and Sub-Agents
Learn how delegation, handoffs, and sub-agents divide work while preserving task ownership, context, state, permissions, and reliable result contracts.
Single-Agent vs Multi-Agent Systems
Learn how single-agent and multi-agent systems differ, what extra coordination costs, and how to choose the simplest architecture that works.