STOP BLAMING THE MODEL: BUILD A BETTER AGENT HARNESS
A thoughtful write-up argues reliable AI agents come from the harness around the LLM, not the model itself. The piece explains why agents fail in production ev...
A thoughtful write-up argues reliable AI agents come from the harness around the LLM, not the model itself.
The piece explains why agents fail in production even when benchmarks look great: LLMs are stochastic, so consistency, tool usage, context control, and recovery must live outside the model. The author calls this the harness — the system that interprets outputs, validates structure, retries, and orchestrates tools.
Read the argument and patterns in the DEV post: Harness Engineering: The Most Important Part of AI Agents. The takeaway: treat the model as an unreliable dependency and engineer compensating controls like any other flaky external system.
Reliability, cost, and safety come from harness design — retries, validation, tooling, and observability — more than from swapping models.
Backend and data teams already own these muscles: orchestration, schemas, idempotency, tracing, and SLOs.
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terminal
Add JSON Schema validation with auto-retry-on-parse-fail; compare success rate, tail latency, and token spend before vs. after.
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terminal
Introduce tool-call timeouts and circuit breakers; chaos-test flaky APIs to verify idempotency, rollback paths, and complete tracing.
Legacy codebase integration strategies...
- 01.
Wrap existing LLM calls behind a single harness service with standardized prompts, schemas, retries, and tracing to reduce drift and hidden couplings.
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Centralize external tool execution with idempotent jobs and rate limiting; publish metrics for failure modes and backoff behavior.
Fresh architecture paradigms...
- 01.
Design the harness first: choose a simple state machine or workflow engine for steps, memory, and error paths; keep the model swappable.
- 02.
Define contracts early: input/output schemas, tool specs, and evaluation metrics; gate releases with harness-level tests.
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