AGENTIC AI: ARCHITECTURE PATTERNS AND WHAT TO MEASURE BEFORE YOU SHIP
A new survey consolidates how LLM-based agents are built—policy/LLM core, memory, planners, tool routers, and critics—plus orchestration choices (single vs mult...
A new survey consolidates how LLM-based agents are built—policy/LLM core, memory, planners, tool routers, and critics—plus orchestration choices (single vs multi-agent) and deployment modes. It highlights practical trade-offs (latency vs accuracy, autonomy vs control) and evaluation pitfalls like hidden costs from retries and context growth, and the need for guardrails around tool actions. Benchmarks such as WebArena, ToolBench, SWE-bench, and GAIA illustrate task design and measurement under real constraints.
It gives a concrete blueprint to structure agent services and choose between single-agent and multi-agent designs.
It clarifies what to measure in production-like settings beyond accuracy, including budgets, safety, and reproducibility.
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Run agents against sandboxed tool APIs with strict latency/token budgets and log retries, tool errors, and context growth to quantify hidden costs.
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Build a reproducible harness mapping your internal tasks to benchmark-style checks (success criteria, constraints, rollback) and compare single-agent vs multi-agent orchestration.
Legacy codebase integration strategies...
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Introduce an agent gateway that enforces allowlists, permission scopes, dry-run modes, and audit logs before granting write access to prod services and databases.
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Start with a centralized single-agent orchestrator and add memory/tooling incrementally, with feature flags and deterministic replay for incident analysis.
Fresh architecture paradigms...
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Design a modular agent stack (planner, memory, tool router, critic) with observability from day one and budget-aware controllers for latency and tokens.
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Pilot offline assistants first (read-only and verification loops), then graduate to online control with guardrails and rollback paths.