AI agents in production: the 2026 architecture that actually ships
Most agent pilots never ship. The 2026 stack that does: task-level tools, an LLM gateway, durable execution, auditable memory, evals and security.
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AI & Automation insights for teams that ship
Shipping LLM features, agents and automations that survive real users — evals, security, cost control and compliance.
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11 articles
Prompt injection in 2026: how to defend AI features and agents
Prompt injection is still the top LLM risk. The threat model, the lethal trifecta and the defences that work: least privilege, isolation and approvals.
Read moreRAG vs fine-tuning vs long context in 2026: how to choose
Long context didn't kill RAG, and fine-tuning isn't a knowledge base. A framework for grounding LLM features in your data, plus the evals to prove it.
Read moreLLM gateways and model routing: stop hard-coding one AI provider
Why startups put an LLM gateway in front of AI features, how model routing cuts cost, and how LiteLLM, Portkey, OpenRouter and cloud gateways compare.
Read moreAI support agents: how to measure ROI before and after launch
Deflection is a vanity metric. Measure AI support agents by verified resolution, cost per resolution and CSAT, and decide whether to buy or build.
Read moren8n vs Zapier vs custom automation for SaaS teams
Compare no-code automation tools and custom workflows for startups — cost, flexibility, observability, and when to graduate from Zapier to code or n8n.
Read moreHow to add AI to your SaaS without breaking trust or performance
A practical checklist for LLM features: scoping, RAG vs. prompt-only, evals, latency, cost, and rollout — written for founders shipping real products.
Read moreLLM API cost optimization for startups: a practical guide
Cut LLM spend without gutting quality — caching, model routing, prompt design, evals, and when smaller models are enough for production SaaS features.
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