AI agent concepts, explained
Plain-English explainers for AI agent concepts: tool use, memory, orchestration, evaluation, safety, refusal policy, stopping conditions, and the rest of the agent stack. Written for non-researchers who need to make build vs buy calls.
AI Agent Pricing in 2026: What Agents Cost Per Month
AI agent pricing pages are designed to look comparable when the models behind them are not. A flat-fee platform at one hundred dollars per month can be cheaper or more expensive than a usage-based platform at five…
AI Agent Platforms with No Vendor Lock-in (2026)
"No vendor lock-in" is one of the most overused phrases on enterprise pricing pages. Every platform has some lock-in. The honest question is which lock-ins you can live with and which would be catastrophic if you had…
AI Agent Platforms with the Best Integrations (2026)
The "most integrations" claim is the cheapest one a SaaS marketing page can make. Counting connectors does not tell you whether the platform can do real agent work inside each one. A platform with five hundred…
AI Agent Update Cycles: Safe Change Management for Production Agents
Production AI agents need updates. Models improve. Prompts get tighter. Tools get added. The team finds a way to make the stopping rule clearer. The question is not "should we update" but "how do we update without…
AI Agent State Management: Memory, Checkpoints, and Durability
The single hardest non-model problem in agent engineering is state. The model is stateless. Every other part of the system that gives it the illusion of continuity, of memory, of resumption, is your code. Get it…
AI Agent Prompt Versioning: Storage, Promotion, Rollback
Code without version control is a hobby. Prompts without version control are a liability. The reason most agent prompts produce silent regressions in week six is not that the prompt got worse; it is that nobody can…
AI Agent Prompt Engineering: 5 Parts of a Production Prompt
Prompt engineering for AI agents is not the same craft as prompt engineering for chatbots. A chatbot prompt shapes one response. An agent prompt shapes a loop: the model picks a tool, reads the result, decides…
Multi-Agent Coordination Patterns: Supervisor, Peer, Market, Shared-State
The intuition that "more agents will do better than one agent" is wrong more often than it is right. Most production multi-agent systems exist because the work has genuine boundaries (different access controls,…
AI Agent Integration Patterns: Webhook, OAuth, MCP, Polling, Queue
Most AI agent failures in production are not model failures. They are integration failures. A webhook arrives twice and the agent acts twice. An OAuth refresh fails silently and the agent runs unauthenticated. A…
AI Agent Data Residency: EU, India, US Architecture Patterns
Data residency is one of the silent gating items for enterprise sales of AI agents. The product can be perfect, but if the prompts leave the EU, the deal dies. This guide is the architecture playbook for…
AI Agent Audit Trails: A Logging Design Guide
An AI agent's audit trail is the difference between "the agent took an action" and "we know why the agent took that action." It is what enables incident response, compliance audits, and the kind of post-hoc analysis…
AI Agent Security Best Practices: A 2026 Production Playbook
Most security guides written for large language models stop at the prompt boundary. They assume a single completion, no tools, no state, no autonomy. That model has not described production deployments for at least…
AI Agent Monitoring and Observability: A 2026 Production Playbook
The first time I shipped an agent without proper observability I did not notice quality degradation for nine days. Token costs were stable, latency was fine, error rates were nominal. The agent was answering…
AI Agent Governance and Compliance: A 2026 Operating Guide
For most of 2024 and the first half of 2025, AI governance for agents was a tomorrow problem. By mid-2025 it had become a this-quarter problem. The EU AI Act began entering force in stages, the NIST Generative AI…
AI Agent Cost Optimization: A 2026 Tactical Playbook
This is not a primer on AI agent pricing models. The taxonomy of per-token, per-task, per-agent, and capability-based pricing already lives at AI agent cost models explained. This piece is the operational sibling.…
AI Agent Watch List: Apartments and Flights
A "watch list" agent is the simplest, most useful agent most people never bother to set up. It polls a small number of listings on your behalf, applies criteria you specify once, and alerts you when something…
AI Agent for Meal Planning: A Weekly Setup
The pitch for a meal-planning agent is simple: 30 minutes of weekly menu work, gone. The trick is that meal planning is bound by hard physical constraints (allergens, what is in the pantry) and soft preferences…
AI Agent Trust Models: Four Levels, Audit Trails, Recovery
The first time an agent does the wrong thing in production is the day a trust model becomes a budget line. Every team eventually writes one. The question is whether you write it before the incident or after. This…
AI Agent Safety and Guardrails: Refusal, Blast Radius, Hostile Input
Safety for AI agents is structurally different from safety for chatbots. A chatbot that says something inappropriate creates a screenshot. An agent that does something inappropriate creates an incident: an email…
AI Agent Failure Modes: 8 Ways Agents Break, Mapped to OWASP
"Why did the agent fail?" is the question every operator asks the first time an agent misses. The answer usually lands in one of eight categories, and they differ enough that "the agent broke" hides the information…
AI Agent Evaluation Metrics: What "Good" Actually Looks Like
"Is the agent any good?" is the question every buyer asks and almost no buyer can answer with a number. The shortage of good answers is not because the metrics are unknown; it is because most vendors publish one or…
AI Agent Benchmarks 2026: 5 That Matter, With October Scores
The five AI agent benchmarks worth reading in October 2026 are SWE-bench Pro (coding), Terminal-Bench (real work in a shell), OSWorld 2.0 (using a computer), tau-bench (customer conversations, and reliability across…
AI Agent Prompt vs LLM Prompt: 5 Real Differences (2026)
The first time someone writes an agent prompt the way they write an LLM prompt, the agent breaks within the first hour of running. Not because the prompt is wrong in a literal sense; it is just shaped for the wrong…
AI Agent for Weekly KPI Reports From Your Stack
The Monday morning KPI summary is the report that should be automated and almost never is. The data exists. The query exists. The template exists. What is missing is the half-hour every Monday that somebody spends…
AI Agent Tool Use Explained: Function Calling, Selection, Recovery
Tool use is what separates a chatbot from an agent. A chatbot talks about sending the email; an agent calls the email-send tool and watches for the result. The mechanism under tool use is function calling,…
AI Agent Reasoning vs Pattern Matching: What Agents Actually Do
Whether AI agents "reason" is a debate that often misses the practical point. The practical point is that different reasoning patterns produce different reliability characteristics on different tasks.…
AI Agent Orchestration Explained: Planner, Executor, Evaluator
Orchestration is the runtime layer that coordinates multi-step agent execution. The LLM thinks; the orchestration decides which step runs next, retries when something fails, evaluates whether the goal is met, and…
AI Agent Myths and Reality: 8 Claims, Debunked
The discourse around AI agents in 2026 carries a lot of myths. Some come from vendor marketing; some come from social-media hot takes; a few are honest misunderstandings of fast-moving terminology. This post takes…