📰 Dev.to · Austin Vance

AI agent security belongs at the runtime-owned tool-call boundary where principal, grant, resource, capability, data flow, and audit get checked.

Coding-agent spend becomes manageable when cost is attached to traces, reviewed by engineering, and enforced through gateway policy.

Agent orchestration works better when loops, retries, fanout, and approvals move from prompts into executable code with a narrow harness boundary.

Build a corrective RAG pipeline with LangGraph that grades retrieval quality, rewrites bad queries, and generates cited answers.

Prompt caching makes context order, provider cache controls, and cache-hit telemetry part of agentic AI architecture.

AI incident management works when agents maintain a shared record of evidence, decisions, owners, approvals, and follow-up work, not just plausible ro

AI agent evaluation has to keep running through traces, online evaluators, human review, datasets, and redeploy gates after release.

Enterprise AI agents are becoming an operating estate, so registry, identity, policy, observability, cost, approvals, and retirement need a control pl

MCP security best practices need runtime drift detection, capability manifests, quarantine, and per-call evidence after tool approval.

AI agent governance works when policy runs at the action boundary before tool calls, writes, spending, approvals, and recovery decisions.

Enterprise AI agents now cross the client runtime, where app state, permissions, approvals, and frontend observability decide whether they can act saf

AI agent cost management belongs in runtime traces, evals, and harness policy, not monthly finance cleanup.

The agent harness is becoming the real lock-in layer for enterprise AI because routing, traces, evals, memory, policy, and credentials live above the

Salesforce MCP moves CRM integration into the agent runtime, where identity, approvals, retries, idempotency, and traces decide whether CRM side effec

AI agent governance belongs in the runtime path, where identity, policy, traces, and approvals meet before production side effects.

AI agent accuracy should be managed as observable production evidence: traces, evals, side-effect receipts, and release gates, not one average score.

Agent rubrics move evaluation from offline scoring into runtime QA with verifier loops, criteria ownership, and observable verdicts.

At LangChain Interrupt, the agent conversation shifted from models to runtime. LangSmith Engine, SmithDB, Deep Agents v0.6, Managed Deep Agents, MCP,

Agentic payments move spending authority into the runtime, where policy, approvals, receipts, and revocation sit before a wallet signs.

Agent UI is runtime infrastructure: event streams give products typed handles for tools, state, approvals, subagents, errors, and observability.

Agent monitoring has become production infrastructure: traces must carry state, side effects, cost, ownership, checkpoints, rollback context, and SRE

Recurring AI agent workflow failures should become named issues with linked traces, owners, regression evals, and release evidence.

Agent observability breaks when traces stop at the MCP tool boundary. Pass W3C trace context through MCP to connect planner, tool, and service spans.

Agentic AI architecture needs model routing, telemetry, and policy so production agents can send each workload to the right model instead of one defau