The Future of AI Agents in Software Development

Every founder we talk to in 2026 has the same question: 'What does an AI agent actually change about how we build software?' The honest answer is that it changes fewer things than the demos suggest — and more things than the skeptics admit. In this piece we'll walk through what we've learned building agentic systems in production for regulated industries, high-growth SaaS, and consumer marketplaces, and where we think the industry is going over the next 24 months.

The first thing worth naming is that most 'AI agent' demos are not actually agents. They are single-shot LLM calls with a system prompt and a JSON schema. That's fine — that's often the right tool — but calling it an agent muddies the water when it comes time to build something that survives production. A real agent, in the way we use the term, is a system that plans, calls tools, observes results, updates its plan, and eventually returns to the user. That loop has failure modes that a single LLM call does not.

The single biggest lesson we've internalized is: evaluation is not optional. If you ship an agent to production without a labeled evaluation set and a way to score model swaps, you are shipping a system whose behavior you cannot reason about. When Claude 4 shipped and we swapped it in for one client, our eval harness caught a 12-point regression on a specific class of contract clauses within an hour. Without the harness, that regression would have hit customers.

The second lesson is that the boring parts matter more than the model. Retrieval quality, chunking strategy, prompt caching, structured output validation, cost budgets, latency budgets, PII redaction, and prompt-injection defenses will determine whether your agent works in production. The model is a component. The system around the model is what people actually experience.

Where does this go? We think the next 24 months look like this. First, model swaps become boring — you'll swap Claude for GPT for Gemini the same way you swap Postgres versions. Second, evaluation tooling becomes as ubiquitous as CI. Third, the interesting agents move from 'answer a question' to 'do a job end-to-end' — book the flight, file the return, close the ticket. Fourth, cost pressure forces a lot of would-be agentic workflows back to deterministic code because it turns out the boring version worked fine.

If you're building an agent right now, the most important thing you can do is invest in your evals before you invest in your prompts. Everything else follows from that.