Most product builders are still thinking about AI the old way: use AI to help you build features (generate code, write copy, speed up support). That is useful, but it is not the real shift.
The shift is this:
Build your product features by compounding AI agents. Not “AI inside a feature,” but “features made of agents.”
When you do this properly, you stop treating AI as a helper and start treating it as a core system primitive, like a database, a queue, or an API layer. The result is simple: more value shipped faster, and often with a step-change in what your product can do.
And here is the reality: it is hard to appreciate the leverage until you try it the right way. When you integrate agents correctly, the power becomes obvious.
So what does “the right way” actually mean?
The New Feature-Building Paradigm
In the agent-native mindset, every new feature begins with a different set of questions:
- What tools do I need to provide so that an agent can do this?
- What should the agent reason about vs. what must be deterministic and controlled via tools?
- What prompt (instructions + constraints) will consistently guide it to the right behavior?
- How do I evaluate it to verify it does what it is supposed to do?
This changes how you design software.
Instead of writing rigid flows for every edge case, you build capable agents with a set of tools, a prompt contract, an evaluation harness, and a loop for autonomy and improvement. That is the new stack.
1) Tools Are the Product Surface Area for Agents
If agents are going to run your features, they need two things: the ability to get the right information, and the ability to act on your system safely.
That is what tools are: controlled interfaces to your product.
The Core Principle
Agent gives you flexibility and adaptability. Tools give you determinism, safety, and control.
If you want reliable agent behavior, you do not “hope the model will do the right thing.” You give it tools that make the right thing the easiest thing.
Practical Tips for Tools
- Make tools small and composable. A tool should do one thing well (e.g.,
get_user_profile,create_invoice,refund_payment) rather than being a giant “do everything” endpoint. - Design tools as contracts. Strong schemas, clear input validation, meaningful error messages, and idempotency where needed.
- Separate “read tools” and “write tools.” Reading is usually safe; writing should be gated, logged, and permissioned.
- Build guardrails into tools, not prompts. If something must never happen (e.g., deleting customer data), enforce it in the tool layer.
- Return structured outputs. Tools should return data the agent can reason over. JSON-like structures are better than free text.
If tools are weak, the agent becomes either dangerously unconstrained or constantly blocked and useless.
Your tools are how the agent becomes a real product component.
2) Prompting Still Matters: Even More Than Before
Yes, prompting is still key. Arguably more key now.
As models get better at following instructions, the prompt becomes less “a suggestion” and more like a specification. It determines whether the behavior is mediocre and inconsistent or robust and repeatable.
Practical Tips for Prompts
There are tons of guides for this on the internet, but here are a few important tips related to agents and tools:
- Write prompts as operating manuals. Role, goals, constraints, what “good” looks like, what “bad” looks like.
- Define decision rules. Example: “If confidence < X, ask for clarification. If tool returns error Y, retry once, then escalate.”
- Force structure. Define a clear output format, separate decision and execution steps, and go for a tool-first behavior.
- Make the tool policy explicit. When to call tools, when not to, how to handle failures.
- Include real examples. Few-shot examples for edge cases can dramatically stabilize behavior.
3) Evaluation Is the Backbone, and It Must Be Integrated into the Product
This is the part most teams under-invest in, and it is the reason many “agent features” remain demos.
Evaluation is not a separate phase. It is part of the system. If agents are performing product work, then evaluations must be automated, continuously running, deeply integrated, and ideally improved with agents themselves.
What “Deeply Integrated Evaluation” Means
Deeply integrated evaluation means every major agent workflow ships with a test harness that captures real traces (including tool calls and outcomes) and turns them into regression tests you can run whenever you change prompts, tools, or models. It also means you measure what actually matters in production, like quality, safety, latency, cost, and user satisfaction, and you have clear failure routing (retry, fallback, escalation, or human review) instead of hand-wavy “it looked fine.”
Just as important, the evaluation layer needs access to the same underlying data the agent had when it produced an answer, so an evaluator can verify the response is grounded rather than merely plausible. A great way to do this is to leverage agents as evaluators, but keep them tightly scoped: give them clear, narrow instructions and dedicated verification tools to check cited sources, cross-reference the answer against your ingested data, and flag anything unsupported or inconsistent.
Practical Tips for Evaluation
- Evaluate in production, both manually and automatically. Support a manual “evaluate this run” trigger for any conversation or workflow, and continuously sample live runs as agents work (random plus risk-based sampling) to run evaluations in the background.
- Turn failures into a regression dataset. Any run that fails evaluation should be saved, together with the exact context the agent had (retrieved chunks, tool outputs, inputs, actions), into an evaluation dataset that you can replay later as regression evaluations.
- Close the loop with evaluator-driven change proposals (human-approved). Give the evaluator dedicated tools to propose concrete fixes, such as prompt edits, improved retrieval settings, or ingestion/data corrections, and store those proposals in a review queue for a human to approve. Once approved, re-run the regression suite to confirm the change actually makes the failing cases pass.
Evaluation is what turns agents from “cool” into “production.”
Toward Autonomy: Less “Human in the Loop” by Design
The goal is not to guide agents constantly. The goal is to make them autonomous: self-validate what they do, self-correct when something goes wrong, self-test changes, and automatically deploy or publish when confidence is high.
In other words, keep humans in the loop only where it matters: reserve manual review for critical or high-risk actions, and let the rest run automatically. That is how agent-native products evolve faster, scale further, and deliver more value as autonomy increases.
Autonomy does not come from a bigger model. It comes from the triad:
Tools + prompting + integrated evaluation
That is the formula.
The Real Takeaway
If you are still thinking “How can AI help me build my product faster?” you are already behind.
The new question is:
How can I build my product with agents as first-class components, designed for autonomy, determinism, and continuous evaluation?
You will only really understand the leverage once you build at least one feature this way end-to-end.
And once you do, it becomes hard to go back.