AI Agent Architecture Patterns: Quick Answers, Components, and Design Choices

By Chris Moen • Published 2026-02-26

A concise guide to AI agent architecture patterns—what they are, when to choose single vs. multi‑agent designs, and how to handle loops, tools, memory, safety, and evaluation.

Breyta workflow automation

Quick answer: What are AI agent architecture patterns?

AI agent architecture patterns are reusable designs for how an LLM-powered agent plans, acts with tools, observes results, and iterates toward a goal. Common patterns include:

  • Single-agent ReAct loop: think, act with one tool, observe, repeat.
  • Retriever-executor: retrieve context, then answer or act.
  • Planner-executor (single agent): produce a short plan, then execute steps.
  • Router-tools: route to the right toolchain or answer directly.
  • Planner plus workers (multi-agent): one planner delegates to executors.
  • Supervisor plus specialists (multi-agent): a router dispatches to experts.
  • Executor plus critic (multi-agent): an actor proposes, a critic verifies.
  • Committee/vote (multi-agent): multiple proposals, one selection.

Key concepts and building blocks

Agent architectures connect an LLM to tools, memory, and control logic. Most reliable systems share these parts:

  • Policy: the system prompt, instructions, and constraints.
  • Control loop: plan, act, observe, reflect, and stop conditions.
  • Tools: typed functions and APIs (search, code, retrieval, execution).
  • Memory: short-lived scratchpad plus selectively retrieved long-term state.
  • Orchestrator: routing, retries, timeouts, approvals, and waits.
  • Safety: input/output filters, access control, egress rules, and audit.
  • Telemetry: traces, run history, costs, and latency metrics.

Choosing the right AI agent architecture pattern

Start with the simplest design that can succeed. Prefer a single agent when one role and a small toolset can achieve the task. Move to multi-agent when you have clear role separation, isolated tool permissions, or you need built-in review and handoffs.

Single-agent patterns

  • ReAct loop: a dependable default for tasks that require a few tool calls and incremental reasoning.
  • Retriever-executor: reduce hallucination by fetching facts first; works well for Q&A and targeted edits.
  • Planner-executor in one: generate a short, revisable plan before acting; good for multi-step jobs.
  • Router-tools: let the agent choose between a small set of well-typed tools or answer directly.
  • Verifier-once: add a lightweight final check for format, policy, or obvious errors.

Multi-agent patterns

  • Planner plus workers: a planner decomposes tasks; workers execute and report back.
  • Supervisor plus specialists: a supervisor routes to domain-specific agents with restricted tools.
  • Executor plus critic: an actor proposes changes; a critic verifies constraints before commit.
  • Red team plus patcher: a tester probes for failures; a fixer attempts targeted corrections.
  • Committee vote: multiple agents propose solutions; a selector chooses or fuses the best.

Planning and control loops

Add planning when tasks span multiple steps or require external coordination. Keep plans short, update them as you learn, and enforce hard stops.

  • Caps: maximum steps, time budget, and per-tool quotas.
  • Retries: backoff on transient failures; avoid infinite loops.
  • Reflection: trigger self-checks after errors or low confidence.
  • Checkpoints: persist state so runs can resume deterministically.
  • Approvals and waits: human-in-the-loop for risky or irreversible actions.

Tool and action management

Tools define the agent’s power and blast radius. Keep them minimal, typed, and auditable.

  • Design a narrow action space with clear names and descriptions.
  • Require structured arguments and strict schemas.
  • Offer dry-run and sandbox modes for write operations.
  • Enforce authentication, quotas, and egress allowlists.
  • Log inputs, outputs, costs, and errors with traceable run IDs.

Memory and context strategy

Use only the context needed for the current step. Separate ephemeral reasoning from durable knowledge.

  • Scratchpad: hidden chain-of-thought or intermediate notes.
  • Working set: entities, goals, current plan, and changed artifacts.
  • Long-term: documents, facts, and prior run summaries.
  • Retrieval: fetch by IDs or search; cap size and add relevance thresholds.
  • Summarize after each step to limit growth.
  • Store structured facts over raw text where possible.
  • Add decay, compaction, and strict size limits.
  • Rebuild context deterministically from references.

Evaluation, safety, and hardening

Evaluation checklist

  • Define golden tasks and measurable success criteria.
  • Track pass rate, latency, cost, and tool error rates.
  • Run A/B tests on prompts, tool sets, and loop controls.
  • Inspect traces to catch bad loops, context bloat, or leakage.
  • Add regression suites for fixes and releases.

Security checklist

  • Filter inputs and outputs to mitigate prompt injection and exfiltration.
  • Isolate secrets; apply least-privilege access to tools and data.
  • Use explicit allowlists for external access and network egress.
  • Validate responses against policies and schemas before acting.
  • Sandbox code execution and file operations.

Framework and stack selection

Choose frameworks and infrastructure that match your constraints, latency targets, and operations model.

  • Typed tool APIs and schemas; clear error handling.
  • Flexible routing and loop control primitives.
  • First-class evaluation and tracing integration.
  • Memory connectors and retrieval support.
  • Guardrails, RBAC, approvals, and auditability.
  • Cloud or on-prem deployment with cost controls.

Common pitfalls to avoid

  • Vague goals and unconstrained prompts.
  • Unbounded loops without time or step caps.
  • Tool sprawl with overlapping actions and permissions.
  • Insufficient observability and missing run history.
  • Secrets embedded in prompts or memory.
  • Ignoring latency budgets and cost ceilings.

Copy-paste blueprint for a coding agent

Use this starter plan to scope, control, and evaluate a repo-editing agent. Adapt to your stack.

Goal

  • Build a coding agent that edits a repo and opens PRs for small fixes.

Scope

  • Allowed tasks: refactors, lint fixes, tests, doc updates.
  • Disallowed tasks: schema changes, secrets, prod deploys.

Tools

  • read_file, write_file_sandbox, run_tests, search_repo, open_pr.

Policy

  • System prompt: objectives, style, constraints, tool rules.
  • Require a plan of 3–7 steps per task before acting.

Loop

  • plan -> act(tool) -> observe -> reflect -> next step.
  • Caps: max_steps=12, max_runtime=5m, max_cost per run.

Memory

  • Working set: goals, plan, changed files.
  • Retrieval: limit to top 10 matches per query.

Safety

  • Dry-run writes, diff review, and unit tests must pass.
  • Block network. Mask secrets. Enforce file allowlist.

Validation

  • JSON schema for tool args and outputs.
  • Static checks and test gate before open_pr.

Observability

  • Log prompts, tool calls, diffs, and metrics with run_id.
  • Emit summary with success flag and PR link.

Evaluation

  • Golden tasks: 20 repo issues. Track pass rate, latency, cost.
  • Regression run on every prompt or tool change.

Where Breyta fits in your architecture

Breyta is a workflow and agent orchestration platform for coding agents. It helps teams build, run, and publish reliable workflows, agents, and autonomous jobs with deterministic execution, clear run history, versioned flow definitions, approvals, waits, reusable templates, and an agent-first CLI.

Breyta is the workflow layer around the coding agent you already use, and it can orchestrate both local agents and VM-backed agents over SSH.

FAQ

What is the simplest reliable agent loop?

A ReAct-style loop works well: plan a step, call one tool, observe, and repeat. Add a step cap, time budget, and retries.

Should I start with single- or multi-agent?

Start with a single agent. Move to stateful multi-agent patterns when roles or tools are clearly separate, or when you need built-in review and handoffs.

How do I keep latency and cost under control?

Trim context, cache retrieval, and use smaller models for routing or classification. Add step caps and budget limits. Batch tool calls when safe.

How do I prevent prompt injection?

Filter inputs, restrict tools, and verify outputs. Use allowlists and policy checks before any external action, and avoid trusting user-provided instructions blindly.

How do I debug agent failures?

Inspect traces and tool logs, reproduce with the same context, and add smaller sub-tasks with stronger validations to isolate faults.