Manual competitive research is a quarterly exercise that's outdated by the time it's filed. The Research Agent turns it into a continuous feed: tracking competitor content, detecting latent competitors, monitoring share-of-model shifts, and surfacing the strategic moves before your team has to discover them.
Every B2B marketing team runs competitive research. Almost none of them do it well. The classic pattern: a marketer spends two days every quarter pulling competitor blog posts into a spreadsheet, building a "what they're doing" deck, presenting it once, then watching it gather dust. By the time the next quarter's deck arrives, the market has moved. The intelligence layer should be continuous, not batched.
This guide walks through automating competitive research with the OpenGraphy Research Agent: what continuous monitoring actually tracks, how latent competitor detection works, the workflow that turns research into shipped content, and the integration points with the rest of the agent stack. By the end, you should be able to replace the quarterly deck with a live, agent-driven competitive intelligence layer.
The Research Agent
The Research Agent is the intelligence layer of the OpenGraphy stack. It monitors named competitors and adjacent brands, tracks content and messaging shifts, runs stress tests on shared queries, and feeds insights to the writing and publishing agents downstream.
What the agent monitors
Three feeds, all continuous: content changes (new blog posts, landing page rewrites, pricing page edits, podcast episodes, video releases), latent competitor detection (brands AI models cite alongside or instead of yours, even if they're not in your traditional category), and share-of-model deltas (week-over-week movement in your share of model versus named rivals across ChatGPT, Claude, Gemini and Perplexity).
Why continuous beats quarterly
The fastest-moving signal in competitive intelligence today is AI model behavior. ChatGPT updates its training and retrieval continuously; Perplexity refreshes citations weekly; Gemini's grounding logic shifts often. A quarterly snapshot is meaningless on this timescale. Continuous monitoring catches shifts in days, not months.
Brand DNA-aware filtering
The Research Agent reads your Brand DNA to filter signal from noise. A new competitor blog post on a topic outside your strategic focus gets ranked low; one inside your priority topics gets surfaced immediately. The DNA layer is what makes the feed actionable instead of overwhelming.
Setting Up Automated Research
Standing up the agent takes most teams under an hour. The setup sequence below is the one that gets to a working monitoring layer fastest.
Step 1 — Define your competitor set
List 5–10 named competitors. Don't stop at the obvious ones — include adjacent-category brands, indirect substitutes, and one or two "who-could-eat-our-lunch" outliers. The agent's latent competitor detection will surface more, but you need a starting set.
Step 2 — Configure monitoring parameters
Pick the cadence (most B2B teams: daily check, weekly digest), the channels to monitor (websites, blogs, LinkedIn, podcast feeds, YouTube), and the alert thresholds (when to notify vs. when to log silently). See How AI Models Reveal Your Hidden Competitors for the latent-detection methodology.
Step 3 — Automate stress tests
Configure recurring stress tests on the queries that matter — typically 30–80 buyer queries that your audience asks ChatGPT, Claude, Gemini and Perplexity. Weekly cadence for competitive categories, monthly for stable ones. See Stress Testing Your AI Presence.
Step 4 — Connect the Sentiment Radar
Configure the Sentiment Radar to track competitive sentiment across AI responses. A drop in your sentiment vs. a rise in a rival's is one of the highest-priority signals to act on.
Step 5 — Wire alerts and digests
Two output streams: real-time alerts for high-priority signals (competitor product launches, sudden share-of-model drops), and a weekly digest summarizing the slow-moving picture. Most teams send the digest to the marketing director and the Slack channel; alerts go directly to the marketer on duty.
From Research to Action
Research without action is overhead. The Research Agent integrates upstream of the writing and publishing layer so that intelligence converts into shipped content with minimal handoff.
The "gap to article" workflow
When the Research Agent identifies a content gap — a topic competitors cover that you don't, or a query AI models answer with rival citations — it can hand the brief directly to the AI Writing Agent. The article ships within hours of the gap being detected. See Building Effective Agent Workflows for the orchestration pattern.
The "competitor moves" workflow
When a tracked competitor publishes a major asset (product launch, executive POV, new pricing page), the Research Agent triggers a response workflow: drafted counter-position article, social variants, AI-visibility audit on adjacent queries. The team reviews and ships within 24–48 hours instead of being caught flat-footed.
The "share-of-model recovery" workflow
When share-of-model drops on a tracked query, the Research Agent triggers an audit: which sources are AI models citing now? What schema/authority signals do those sources have that your content lacks? It produces a punch list of fixes and routes them to the GEO Agent. See How to Rank in AI-Generated Lists.
What the Agent Is Not (And Why That's Important)
The Research Agent is an intelligence and routing layer. It's not a strategic decision-maker. It surfaces signals; humans decide what's worth responding to. Two boundaries to keep in mind:
It does not write your strategic POV
If a competitor launches a product, the agent surfaces it and can draft a response — but the strategic angle (do we attack, ignore, embrace?) is a human call. Treat agent output as the briefing material, not the verdict.
It does not decide which competitors matter
The agent monitors what you tell it to monitor, plus the latent competitors it detects. The strategic call to add or drop a competitor is yours. Review the competitor set quarterly.
FAQ: Automated Competitive Research
What does competitive research automation actually monitor?
Three streams: competitor content and messaging changes, latent competitors (brands AI models cite alongside yours), and share-of-model deltas across AI platforms. The OpenGraphy Research Agent runs all three continuously.
How is this different from manual quarterly research?
Cadence and integration. Manual research produces a deck once a quarter; automated research produces a daily intelligence feed that integrates directly with the writing and publishing agents, so insights become shipped content instead of slide decks.
Can the agent detect competitors I don't know about?
Yes — that's the latent competitor detection feature. The agent monitors which brands AI models cite alongside yours on key queries; some will be brands you've never tracked. See How AI Models Reveal Your Hidden Competitors.
How frequently are stress tests run?
Weekly for competitive categories, monthly minimum for stable ones. Configurable per query. The agent recommends the cadence based on movement velocity it detects.
Does it integrate with our existing competitive intelligence tools?
Yes — the Research Agent ingests data from any tool with an API or webhook (Crayon, Klue, Kompyte). It treats them as additional inputs alongside its own monitoring.
How do I act on the findings?
Two output streams: real-time alerts for high-priority signals, weekly digests for the slow-moving picture. The agent can also auto-route briefs into the AI Writing Agent for content responses, closing the research-to-shipped-content loop.
Going Further
- How AI Models Reveal Your Hidden Competitors — the latent-detection methodology.
- Stress Testing Your AI Presence — the query-level intelligence layer.
- Monitoring Brand Sentiment in AI Responses — the sentiment radar.
- Building Effective Agent Workflows — the orchestration patterns that turn research into action.
- Share of Model: The New Market Share Metric — the metric the agent monitors most closely.
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