Agents & AI Engineering
AI engineering is making a model do the same correct thing twice: orchestration, evaluation, and the failure modes that appear once an agent has permissions. Heavy emphasis on security, because an agent that can act is an agent that can be exploited.
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Multi-Agent Orchestration: A Practical Guide for Dev Teams
Multi-agent orchestration coordinates specialized agents into one flow. Add agents only when a task truly needs splitting, not because more sounds better.
Oct 10, 2026 · 8 min read
Agent Orchestration Patterns: Sequential, Parallel, and Hierarchical
Agent orchestration patterns come in three shapes: sequential, parallel and hierarchical. Pick the simplest one, then put a gate at every hand-off.
Oct 10, 2026 · 7 min read
Single-Agent vs Multi-Agent: When You Actually Need Orchestration
Single agent vs multi agent: start with one agent, which is cheaper and easier to debug, and split only for a concrete reason you have measured.
Oct 10, 2026 · 6 min read
The Orchestrator-Worker Pattern, Explained
The orchestrator worker pattern has one lead agent plan and delegate to specialist workers, and it needs a hard limit on delegation rounds or it loops.
Oct 10, 2026 · 6 min read
Why Multi-Agent Systems Fail (and How to Catch It)
Multi-agent failure modes such as loops, bad hand-offs, cost blow-ups and runaway orchestrators live in the glue between agents. Catch them with evals.
Oct 10, 2026 · 6 min read
Zero Trust for AI Agents: Why One Check at the Door Fails
Zero Trust for AI agents checks each tool call, not only the login. Why one check at the door fails, the seven controls, and which actions to wrap first.
Oct 9, 2026 · 8 min read
AI Agent Sandbox Escape vs Tool Abuse: Know the Difference
An AI agent sandbox escape is one failure, and five others get called by its name. Tell it apart from tool misuse, prompt injection and over-broad keys.
Oct 9, 2026 · 6 min read
AI Agent Kill Switch: What to Build Before You Grant Access
An AI agent kill switch is more than a button. The seven parts it needs, who can pull it, how to drill it, and why a stop mid-task can leave a mess.
Oct 9, 2026 · 6 min read
AI Agent Permissions: Enough Access to Help, Not to Harm
AI agent permissions sized to one task: access, action and duration, an authority ladder, just-in-time keys, and what to do when your app can't go narrow.
Oct 9, 2026 · 6 min read
AI Agent Forensics: Prove What Your Agent Actually Did
AI agent forensics rebuilds what an agent did after an incident: one trace ID per run, what a defensible trail holds, and a six-question audit-trail test.
Oct 9, 2026 · 6 min read
AI Intelligence Costs: Your Automation Math Is Out of Date
AI intelligence costs now list at $2 per million input tokens, but Argon's rate is introductory. Three ways cheaper runs move your automation line.
Oct 7, 2026 · 8 min read
Cheap AI Security Risk: More Agents, Bigger Attack Surface
Cheap AI security risk grows with every extra agent run. Why small failure rates become big counts, which OWASP risks scale, and what controls cost.
Oct 7, 2026 · 7 min read
The True Cost of an AI Agent (and How to Calculate It)
The true cost of an AI agent is its full monthly cost divided by accepted results. The cost model, a worked example, and why acceptance rate matters.
Oct 7, 2026 · 6 min read
AI Automation Threshold: When a Workflow Is Worth It
An AI automation threshold model for one workflow: score six factors, cap autonomy by the weakest safety score, then test the economics in a pilot.
Oct 7, 2026 · 7 min read
AI Vendor Lock-In: 7 Signs One Model Owns Your Workflow
AI vendor lock-in turns a price rise into a bill you cannot negotiate. Seven warning signs, five contract terms to settle, and how to cut the dependency.
Oct 7, 2026 · 7 min read
AI Model Lifecycle: Your AI Stack Has an Expiration Date
The AI model lifecycle ends in retirement, with at least 60 days' notice from Anthropic. What breaks when your model moves on, and how to keep running.
Oct 6, 2026 · 8 min read
Claude Sonnet 4.5 Retirement: Your Migration Checklist
Claude Sonnet 4.5 retirement is November 30, 2026. Find every caller, fix settings Sonnet 5.5 rejects, test real tasks, and plan a rollback that still exists.
Oct 6, 2026 · 7 min read
Why AI Model Upgrades Break Production Workflows
AI model upgrades can break production workflows without a single error. What drifts, the hidden dependencies, and how to upgrade with acceptance criteria.
Oct 6, 2026 · 7 min read
LLM Regression Testing: Prove a New Model Is Better
LLM regression testing shows whether a new model is better at your work. Build a golden task set, measure more than accuracy, and ship only past the gates.
Oct 6, 2026 · 7 min read
Model-Agnostic AI Architecture: Keep Your Workflow Portable
A model-agnostic AI architecture keeps rules, tools and tests outside any one model, so a price change or retirement means a test run, not a rebuild.
Oct 6, 2026 · 7 min read
How to Evaluate Prompts (Before They Break in Production)
How to evaluate prompts: test every version against a golden dataset, score it with a task-fit metric, then version so bad changes roll back fast.
Oct 5, 2026 · 8 min read
LLM Output Scoring: Four Methods and When Each Breaks
LLM output scoring: exact match, rubric, LLM-as-judge, and embedding similarity. What each method measures, and where each one quietly breaks.
Oct 5, 2026 · 5 min read
Prompt Regression Testing: Catch the Change That Broke It
Prompt regression testing runs your golden dataset through the old and new prompt version, then blocks the ship if scores cross a threshold you set.
Oct 5, 2026 · 5 min read
Prompt Versioning: Treat Your Prompts Like Code
Prompt versioning means giving every meaningful change a version number, a changelog line, and an owner — the same discipline you already apply to code.
Oct 5, 2026 · 5 min read