The gaps in governance. The limits of RAG. Why SLMs matter. The failure economics nobody calculates. The unglamorous engineering that makes agents work in production. We write about all of it — sharing practical lessons from our work as a small team in India.
The gaps in agentic AI — governance, failure economics, RAG limits, SLMs, and what the hype misses.
The microservices playbook — retry, circuit breaker, saga, graceful degradation — assumes deterministic services. Agents are non-deterministic autonomous actors. Here's why the patterns we know don't transfer.
Read more →Estimate the impact, likelihood, frequency, uncertainty, and recovery cost of an incorrect action. The right assessment depends on what the system can do.
Read more →A thought experiment about bounded tasks, accountability, review, and the costs of delegating work to AI systems.
Read more →A practical review of scope, representative evidence, oversight, recovery, and ownership before expanding an AI system’s use.
Read more →Evaluation should reflect intended tasks, difficult cases, user impact, and the limits of available evidence.
Read more →Connectivity, language, accessibility, device access, and cost vary. Validate assumptions with the people a product is meant to serve.
Read more →A balanced view of explicit rules, AI-assisted review, accountability, and human judgment in consequential workflows.
Read more →Before you build a multi-agent system, ask: can a single agent with good tools do the job? Multi-agent systems add coordination overhead, communication failures, and cascading errors.
Read more →General principles for containing unexpected behavior, assessing impact, recovering, communicating, and learning.
Read more →Durable digital products depend on supportability, accessibility, clear ownership, and adaptation to changing needs.
Read more →Stories can make unfamiliar technology approachable when paired with accurate explanations and clear limitations.
Read more →We're repeating the same mistake from every technology shift — taking old paradigms, adding "AI agent," and shipping it. Here are the 5 mistakes teams make when building agents.
Read more →Before you build an agent, ask five questions. If you can't answer all five, you don't need an agent — you need a simpler solution.
Read more →Each era of computing introduced a new relationship between human and machine. Agents are the fourth era — and they're fundamentally different from everything that came before.
Read more →Practical questions about observable behavior, accountability, limits, and correction in AI-enabled systems.
Read more →Learning about technology, accessibility, and the contexts in which digital tools are used.
Questions to ask when a digital experience may be used with unreliable or unavailable internet access.
Read more →AI literacy includes system limitations, generated errors, data choices, and ethical questions.
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