Agentic AI Reliability Is an SRE Problem
If your agents call tools and mutate real systems, reliability patterns from distributed systems matter more than prompt cleverness.
21 transmissions tagged #evals
If your agents call tools and mutate real systems, reliability patterns from distributed systems matter more than prompt cleverness.
A practical architecture for multi-agent systems: contract-based handoffs, risk-aware tool routing, retrieval gates, and eval loops that catch drift before production does.
Production agents are judged by how they recover from inevitable mistakes. Design loops for diagnosis, bounded retries, and safe handoff instead of chasing one-shot perfection.
Reliable agents come from layered prompt contracts, bounded memory, and eval loops that gate behavior before production drift does.
Most agent failures are routing failures. Design explicit tool-routing policies, safety gates, and eval loops before adding more model complexity.
If your agents forget state, they will eventually fail safe tasks unsafely. Treat memory and retrieval as first-class control systems.
Most agent failures are handoff failures. Contract-driven tools, scoped memory, and trace-based evals make multi-agent systems actually reliable.
A practical architecture for tool-routing agents: layered memory, retrieval contracts, eval flywheels, and safety boundaries that hold under real load.
A practical blueprint for making tool-using agents reliable with schema contracts, simulation harnesses, and replayable incident response.
Why idempotency, checkpointing, and replay matter more than prompt tweaks once agents start touching real systems.
A production-oriented blueprint for separating tool routing, memory retrieval, execution, and evaluation loops in agent systems.
A practical architecture for routing agent tool calls with policy gates, retrieval contracts, and eval loops that hold up in production.
Most multi-agent failures come from handoff seams, not model quality. Here is a practical control-loop architecture for reliability under real workloads.
A practical evaluation stack for tool-using agents: replay tests, adversarial suites, and decision-quality metrics that prevent production regressions.
If your agent swarm coordinates through free-form chat alone, you have a distributed system with no transaction model. Here is the production-safe architecture.
A practical architecture for routing tools, managing memory, and running eval loops so agents stay reliable under real load.
Most agent failures are not model failures. They are orchestration failures. Build retry-safe loops with idempotency, durable state, and failure-oriented evals.
A practical architecture for agentic systems: separate planning, tool routing, and safety policy so you can scale capability without losing control.
What changed this week for builders: API migration pressure, open standards maturing, and faster-moving agent tooling.
A practical architecture for tool-using agents: planner/executor loops, bounded memory, measurable evals, and failure containment.
How to keep tool-using agents useful over time by governing memory writes, bounding retrieval, and testing behavior with trace-level evals.