Multi-Agent Systems
Coordination, hand-off and whether more agents actually help.
4 to work through
-
advanced
A research assistant runs a planner that fans out to four worker agents and a synthesiser that writes the final answer. One worker retrieves the wrong document and returns a fluent confident summary of it. What happens downstream and what stops it?
2 min answer -
advanced
A team proposes decomposing a workflow into several specialised agents that coordinate. What justifies this over a single agent with more tools, and what does it cost?
2 min answer -
advanced
A team proposes five specialised agents that collaborate to handle a customer request. Assess.
2 min answer -
advanced
Interview prompt. A supervisor agent fans out to five worker agents that each call tools with real side effects - creating tickets, sending emails, updating records. One worker fails at its fourth tool call, after two of those side effects have already happened. Tell me what the system does next.
3 min answer
2 terms in this topic
Agent Handoff
The transfer of a task and its context from one specialised agent to another, and the point at which multi-agent systems most often lose information.
conceptSubagent Context Isolation
The property that makes multi-agent systems worth their cost - each subagent explores with its own context window and returns only a condensed result…
Neighbouring topics
AI-Era Architecture
General material on architecting systems that include models.
LLM Application Architecture
The shape of a production system with a model in the request path.
RAG Architecture
Retrieval, grounding, citation and the permissions RAG can enforce.
Vector Databases
Approximate nearest-neighbour search, filtering and re-indexing.
Embeddings
Dense representations, model coupling and the migration they imply.
Chunking & Retrieval
Structure-aware splitting, hybrid search and why chunking dominates quality.
Reranking
Cross-encoders improving precision more than a bigger embedding model.
Model Selection
Capability, latency, cost and the evaluation that decides between them.
AI Gateways
Centralised routing, keys, quotas, caching, logging and safety policy.
Prompt & Version Management
Prompts as reviewed, versioned, evaluated production configuration.
Agent Architectures
Loops, planning, memory and the boundaries an agent must not cross.
Tool Calling
Typed tool interfaces, narrow parameters and per-tool authorisation.
LLM Evaluation
Held-out sets, rubric judging, CI gates and production sampling.
AI Observability
Logging prompts, versions, retrieved context and cost per request.
Guardrails
Deterministic checks on input and output that fail closed.
Prompt Injection Defence
Breaking the private-data, untrusted-input, outbound-channel combination.
AI Cost Management
Token accounting, routing, caching and the context-window budget.
Human in the Loop
Gating by reversibility and blast radius, and avoiding approval fatigue.
ML Platform
Feature stores, training pipelines, registries and deployment.