I Built a Market Intelligence Agent That Learns With Hindsight

Turning Noisy Press Pages Into Hindsight-Backed Competitor Trend Reports
Competitive intelligence sounds simple until you actually try to build it. Every week, a competitor publishes something new, an RSS feed produces another link, and an LLM can summarize what happened. But the difficult question is not “What did they announce?” It is “How does this announcement relate to everything they have done before?”
That was the problem I wanted to solve with a market-intelligence pipeline backed by Hindsight.
The system starts with a roughly 200-word description of the company and turns it into a watch profile containing its offerings, target customers, keywords, and questions worth monitoring. It then collects competitor pages and RSS feeds, filters out URLs it has already processed, extracts article content, and converts the new articles into structured events such as pricing changes, product launches, partnerships, funding, and hiring.
The interesting part begins after extraction.
Rather than storing entire articles in memory, I retain structured events containing the date, competitor, event type, summary, why the event matters to the company, signal strength, and keywords. Stable document IDs prevent the same event from being duplicated when stages are rerun. This makes the memory useful as historical intelligence rather than simply becoming another document store.
This is where Hindsight fits naturally into the architecture. Recall retrieves relevant history for each competitor, while reflect looks across the entire memory to identify broader market trends and recurring patterns. The important distinction is that these answer different questions: recall asks “What has this competitor done before?”, while reflection asks “What is happening across the market over time?”
There is also a subtle but critical detail: recall happens before today's events are retained. Otherwise, the system can retrieve the event it has just written and incorrectly conclude that today's announcement is a historical repeat. That ordering became a correctness constraint rather than an implementation detail.
In one test scenario, a competitor had previously introduced a free AI tier and then cut prices by 30%. When the same competitor later announced unlimited AI resolutions for a flat monthly fee, a stateless model could describe the announcement, but Hindsight provided the historical context needed to identify it as an escalation and connect it to a broader market movement toward flat AI pricing.
What I found most valuable about Hindsight was not simply the ability to retain information. It was the ability to make historical context part of the reasoning workflow without forcing the entire application to become an AI-driven system.
Deduplication remains deterministic. Keyword trends remain arithmetic. Validation remains code. Hindsight is used specifically where the question requires meaning, context, and time.
That separation made the pipeline easier to reason about—and made the resulting reports much more useful.
The bigger lesson for me is simple:
Good agent memory isn't about remembering everything. It's about remembering the right things so that today's information can be understood in the context of yesterday's.

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