Agentic
AI Agents Agent Memory Patterns: What to Persist, What to Forget
Agent memory is a policy, not a bigger context window. Persist identifiers and approved facts; forget scratch and failed conclusions; promote to long-term only on purpose.
AI Agents Golden Sets from Production Failures: Turn Bad Runs into Regression Fuel
Turn a bad production agent run into a regression test by harvesting the trace into a stubbed fixture with a clear pass criterion.
AI Agents Multi-Agent Handoffs Without Lost Context
More agents are not maturity. Typed handoff packages — goals, artifacts, failures, budget remaining — are how multi-agent systems keep context without sharing a contaminated brain.
AI Agents Prompt Injection for Tool Agents: Stop Text from Becoming Actions
Defend production agents that read email, tickets, or pages by fencing untrusted text as data and blocking injection paths into tool calls.
AI Agents Cost Controls for Agent Fleets: Budgets, Caps, and Kill Switches
Token spend is a product feature. Per-run budgets, model tiers by state, revision caps, and kill switches keep agent fleets from becoming surprise invoices.
AI Agents Idempotent Agent Tool Writes: Retries Without Double Emails or Double Charges
Make agent tool writes safe on timeout by minting runtime idempotency keys once, then sharing them across model, harness, and HTTP retries.
AI Agents The Fractional AI CTO Model: When You Need Architecture, Not Another Chatbot
A fractional AI CTO is architecture and operating cadence for teams building agentic IP — not a chatbot retainer. When to hire one, what they own, and how it differs from a pilot.
AI Agents Scoping an Agentic Pilot That Proves Value in Five Days
A good agentic pilot is one sentence, real data, an evaluator, and a cage — finished in five days for $1,500. Here is how to scope it so you learn something true.
AI Agents Pin the Model, Gate the Upgrade: Catch Agent Drift Before Customers Do
Yes—pin production agents to explicit model IDs. Floating aliases change behavior with no deploy. Upgrade only through a golden-set gate you actually run.
AI Agents Observability for Agents: Traces, Scores, and the Dashboard Ops Actually Reads
Provider dashboards will not catch silent wrongness. Agent observability means traces with states and tool calls, evaluator scores online, and a weekly dashboard humans actually use.
AI Agents Tool Schemas Agents Follow: Descriptions, Enums, and Killing the Omnibus Tool
Agents invent arguments when schemas are vague. Write JSON Schema like agent UX—enums, required fields, property descriptions—and kill the do_anything tool.
AI Agents When Not to Build an Agent (And What to Build Instead)
Agents are for uncertain paths with clear criteria. If the path is known, build automation. If criteria are mush, fix the process. Here is how to choose without theater.