Prompt Architecture Is the Control Plane of Agent Systems
Useful agent systems are not held together by one giant system prompt. They are held together by routing, bounded memory, explicit tool contracts, and evals that watch the whole loop.
#evals
53 transmissions tagged #evals
AI Trends: Enterprise Control Planes, Agent Runtimes, and the Compliance Squeeze
The practical AI signal this week: enterprises want fewer point tools, agent runtimes are becoming real infrastructure, open-source builders are codifying self-improving skills, and regulators are moving closer to platform-level oversight.
Agent Evals Need Failure Maps, Not Just Scores
Production agent evals get useful when they score outcomes, inspect traces, and turn repeated failures into architectural changes.
AI Trends: Productized Agents, Harder Benchmarks, and the Compliance Clock
The useful signal this week: consumer AI products are becoming agent systems, orchestration frameworks are consolidating, evals are exposing the harness layer, and regulation is getting uncomfortably concrete.
Agent Tool Routing Needs a Control Plane
Practical patterns for routing tools, structuring memory, and containing side effects in real agent systems.
Agent Memory Is a Reliability System, Not a Recall Feature
Long-term memory helps agents only when writes are selective, retrieval is verifiable, and stale facts are treated as operational risk.
Good Agent Evals Grade the Whole Loop
Single-answer scoring misses what makes agents dangerous or useful. The right evals score trajectories, side effects, and repeatability across the whole execution loop.
Agent Evals Are the Runtime for Reliable Orchestration
Why production agent systems need continuous evaluation across routing, memory, tools, and guardrails instead of a single task-success metric.
Context Is Not Memory for Agent Systems
Practical patterns for separating live context from durable memory so agents retrieve the right facts, use the right tools, and fail in auditable ways.
Capability Envelopes: The Missing Safety Layer in Agentic AI
Why reliable agents need explicit capability boundaries, approval ladders, and trajectory evals instead of bigger prompts.
Agent Reliability Comes From Verifiers, Not More Planning
The difference between a demo agent and a production agent is not better planning. It is a runtime built around verifiers, checkpoints, and disciplined recovery loops.
Agent Memory Needs a Schema, Not a Scrapbook
Good agent memory is not a giant transcript dump. It is a typed system with admission rules, retrieval policy, and evals that prove the right facts arrive at the right time.
Daily AI Trends: model governance, commerce agents, and voice evals
OpenAI is making model behavior more legible, commerce agents are moving closer to production, voice-agent evals are getting sharper, and GitHub attention is consolidating around real agent runtimes.
Agent Prompts Need Architecture, Not Just Instructions
Reliable agents come from prompt architecture: clear policy layers, typed tool contracts, explicit handoff rules, and evals that measure behavior against those boundaries.
Your Agent Needs a Router Before It Needs More Tools
Most agent failures are routing failures. Better tool policy, bounded loops, and explicit safety checks beat handing the model a larger toolbox.
Reliable Agents Verify Every Tool Call
Most agent failures are not planning failures. They are verification failures. Treat every tool call as a state transition that must prove it actually changed the world the way you intended.
Your Agent Needs a Control Plane
Practical patterns for routing tools, writing memory, running eval loops, and setting hard safety boundaries around agent systems.
Multi-Agent Systems Fail at the Handoffs
Most multi-agent failures are not model failures. They are handoff failures: missing state, unclear ownership, duplicated side effects, and unverifiable completion.
Tool Calls Are Side Effects: Why Agent Reliability Starts With Contracts
The hardest part of agent engineering is not getting a model to call a tool. It is making tool use safe, predictable, and recoverable under real failure conditions.
Memory Is a Query Plan for Agents
Useful agents do not need more memory dumped into context. They need a retrieval plan that decides what to fetch, when to trust it, and how to verify it.
Planner, Router, Verifier: A Better Control Loop for Agentic AI
Reliable agents emerge when planning, tool routing, memory, and verification are treated as separate control surfaces instead of one giant chat loop.
Agentic AI Needs a Verify Phase, Not Just a Bigger Prompt
The most useful agent pattern is no longer think-act. It is plan, act, verify, and only then commit to success.
AI Trends Roundup: Sonnet 4.6, agent evals, and browser-native tooling
A practical look at Claude Sonnet 4.6, the rise of agent eval tooling, and why browser-native agent infrastructure is gaining momentum.
Multi-Agent Systems Fail at Boundaries, Not in Demos
The hard part of agentic AI is no longer getting one model to act. It is making delegation, memory, tools, and evaluation behave when the system leaves the happy path.
Daily AI Trends: Evals Crack, Memory Hardens, and GUI Agents Move Into the Page
Today’s real signal for builders: web-enabled evals are getting fragile, orchestration stacks are becoming more opinionated, and practical agent infrastructure is showing up in the repos developers are actually starring.
Routing and Memory Contracts: A Practical Blueprint for Agentic AI That Doesn’t Drift
A production-focused pattern language for agent orchestration: deterministic routing, memory contracts, bounded autonomy, and trace-based eval loops.
Agentic AI Needs a Control Plane, Not Just Better Prompts
Why production agents fail, and how control planes for planning, tool execution, memory, and evals reduce cascading errors.
Agentic AI Reliability Loops: Making Multi-Agent Systems Survive Real Production Traffic
A practical reliability blueprint for multi-agent systems: durable state, idempotent tools, bounded retries, and eval gates tied to real traces.
Budget-Aware Tool Routing for Agentic AI: Fast Paths, Safe Paths, and Measurable Drift
A practical routing architecture for agents: classify intent, score risk, enforce budgets, and evaluate full traces so tool use gets faster without becoming fragile.
Agent Evals Need Incident Budgets, Not Just Accuracy
Why production agents should be evaluated like distributed systems: trajectory-level scoring, failure taxonomies, and explicit incident budgets.
Shadow Mode for Agentic AI: How to Ship Autonomy Without Gambling Production
A practical rollout pattern for multi-agent systems: replay evals, policy gates, and canary promotion instead of all-at-once autonomy.
Agentic AI That Holds Up: Memory Contracts and Eval Gates
A practical architecture for multi-tool agents: route with explicit contracts, retrieve with budgets, and ship through eval gates.
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.
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.
Agent Memory Is the Control Plane
If your agents forget state, they will eventually fail safe tasks unsafely. Treat memory and retrieval as first-class control systems.
Why Multi-Agent Systems Fail at Handoffs (and How to Fix Them)
Most agent failures are handoff failures. Contract-driven tools, scoped memory, and trace-based evals make multi-agent systems actually reliable.
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 Contract Tests, Not Just Better Prompts
A practical blueprint for making tool-using agents reliable with schema contracts, simulation harnesses, and replayable incident response.
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 Control Planes: Practical Patterns for Routing, Memory, and Eval Loops
A production-oriented blueprint for separating tool routing, memory retrieval, execution, and evaluation loops in agent 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 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.
Daily AI Trends: Responses Migration, MCP Governance, and the New Agent Eval Stack
What changed this week for builders: API migration pressure, open standards maturing, and faster-moving agent tooling.
Agentic AI Control Loops That Survive Production
A practical architecture for tool-using agents: planner/executor loops, bounded memory, measurable evals, and failure containment.
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.