Uncertainty-First Tool Routing for Agentic AI
A practical pattern for routing tools, memory retrieval, and eval loops by uncertainty instead of raw confidence.
#safety
18 transmissions tagged #safety
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.
Memory Tiers Stop Failure Cascades in Multi-Agent Systems
Most agent failures are not single bad calls. They are memory propagation bugs. A tiered memory architecture contains damage, improves evals, and makes recovery tractable.
Contract-First Agent Orchestration: Build Loops That Fail Safe
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.
Recovery Loops Beat First-Pass Accuracy in Agentic AI
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.
Prompt Architecture Is Your Agent Control Plane
Reliable agents come from layered prompt contracts, bounded memory, and eval loops that gate behavior before production drift does.
Tool Routing Is the Reliability Lever in Agentic AI
Most agent failures are routing failures. Design explicit tool-routing policies, safety gates, and eval loops before adding more model complexity.
Agentic AI Memory Architecture That Survives Production
A practical architecture for tool-routing agents: layered memory, retrieval contracts, eval flywheels, and safety boundaries that hold under real load.
Agentic AI Needs Durable Execution, Not Just Smarter Prompts
Why idempotency, checkpointing, and replay matter more than prompt tweaks once agents start touching real systems.
Agentic AI Tool Routing: Build a Policy-First Control Plane
A practical architecture for routing agent tool calls with policy gates, retrieval contracts, and eval loops that hold up in production.
Multi-Agent Systems Fail at the Seams: Build Control Loops, Not Chat Loops
Most multi-agent failures come from handoff seams, not model quality. Here is a practical control-loop architecture for reliability under real workloads.
Agentic AI Memory Architecture That Doesn’t Lie
A practical blueprint for agent memory layers, retrieval contracts, and safety boundaries that hold up under production load.
Agentic AI Evals That Catch Real Failures
A practical evaluation stack for tool-using agents: replay tests, adversarial suites, and decision-quality metrics that prevent production regressions.
Multi-Agent AI Needs Distributed Systems Rules, Not Better Vibes
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.
Agentic AI Control Loops That Hold Up in Production
A practical architecture for routing tools, managing memory, and running eval loops so agents stay reliable under real load.
Retry-Safe Agentic Systems: How to Keep Tool-Using Agents from Double-Spending Reality
Most agent failures are not model failures. They are orchestration failures. Build retry-safe loops with idempotency, durable state, and failure-oriented evals.
Policy Planes for Agentic AI: Routing, Memory, and Evals That Survive Contact with Reality
A practical architecture for agentic systems: separate planning, tool routing, and safety policy so you can scale capability without losing control.
Agent Memory Is a Database Problem: Write Paths, Retrieval Budgets, and Eval Gates
How to keep tool-using agents useful over time by governing memory writes, bounding retrieval, and testing behavior with trace-level evals.