A lot of AI product work gets framed as a model problem. The model needs to be smarter. The context window needs to be bigger. The prompt needs more examples.
Sometimes that is true. More often, the product has an input problem. The system is asking a model to make a decision without giving it the current, structured, trustworthy information a person would need.
That is why I built a research toolkit for agents. The goal was not to make the agent sound smarter. The goal was to make the product more grounded.
The Product Problem
Agents are useful when they can reason over the same reality the business operates in. If an agent is analyzing a market, it needs current prices, filings, macro data, and news. If it is helping a healthcare workflow, it needs the right record, the right policy, the right permissions, and the right timestamp.
The simple answer is to give the agent web search and hope. That works for exploration, but it is weak product infrastructure. Search results are noisy, inconsistent, stale, and often not shaped for the decision the product needs to support.
Useful AI products need data access that is scoped, typed, current, observable, and easy to audit.
Design the Inputs Like Product Surfaces
The toolkit exposed a small set of tools, each with a narrow job:
- Current facts. Quotes and time series for the entities the system needs to understand.
- Official records. Filings and structured documents that should not be approximated from memory.
- Macro context. External indicators that affect how a decision should be interpreted.
- Fresh narrative context. News and updates ingested on a schedule instead of scraped at random.
- Synthesis. A brief generator that can combine the inputs and show what it used.
The important product decision was keeping each tool narrow. A broad "research the market" tool is hard to trust. A quote tool, filing tool, macro tool, and news tool are easier to test, cache, rate-limit, and explain.
Freshness Is a Feature
Anyone building AI into real workflows has to care about freshness. It is not enough for a model to know general facts. The system has to know whether the information it is using is current enough for the decision at hand.
For news, I prefer ingestion over live scraping. A scheduled pipeline can fetch, deduplicate, timestamp, and store articles before the agent needs them. That means faster responses, fewer runtime dependencies, and a clearer audit trail.
The pipeline itself becomes part of the product. You can observe failed feeds, stale caches, parsing errors, and source coverage. Without that visibility, the agent's confidence can hide a broken data supply chain.
Composition Beats One Giant Context Dump
I built the research layer as a plugin because products should be able to compose capabilities without turning every agent into a massive bag of context. The agent should pull the right tool when the task requires it, not carry every possible fact all the time.
That separation also makes the product easier to reason about. If a brief is wrong, you can inspect whether the quote was stale, the filing query failed, the news cache missed something, or the synthesis step overreached. Debugging becomes possible because the system has edges.
Fallbacks Matter
A product that depends on AI should still know how to degrade. The brief generator can use a frontier model when it is available, but the system should still produce a deterministic fallback when it is not.
That fallback may be less elegant, but it keeps the workflow alive. In a customer-facing workflow, graceful degradation is not a nice engineering extra. It is what prevents one unavailable dependency from becoming a broken customer promise.
The Product Lesson
Useful AI products will not come only from bigger models. They will come from reliable systems around the model: cleaner inputs, stronger permissions, fresher data, more visible provenance, and workflows that know when to ask for help.
Context windows will keep growing. That does not remove the team's responsibility to decide what context should matter.
What I Would Build Toward
The direction that seems most useful is not "agent with internet access." It is "agent with governed access to the right business systems." That means data contracts, source freshness, provenance, audit logs, and a user experience that makes uncertainty visible.
Give agents reliable inputs and they become more useful. Give them a bigger pile of undifferentiated context and the work often becomes harder to evaluate.