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AI Agent Automation Case Study
Kenstin Technologies shipped a two-agent automation in n8n: one stage pulls fresh web and Reddit signals; the next distills them into crisp summaries users can act on-built for volatility, not yesterday’s cache.
7
Weeks
100%
Completion
4
Tech Used
Why teams choose this build
Concrete scope signals from the engagement-structured for evaluation, not vanity metrics.
Architecture
Two-agent pipeline
Orchestration
n8n workflows
Freshness priority
Live source pulls
Project foundation
We start with scope clarity, challenge mapping, and execution guardrails before implementation begins.
Stakeholders wanted an agent-style workflow that could track fast-moving topics-product launches, competitor chatter, community sentiment-without manual tab-hopping. Kenstin implemented a pipeline orchestrated in n8n that emphasizes recency, source diversity, and readable output suitable for daily briefings rather than raw link dumps.
Freshness was non-negotiable: stale summaries erode trust, especially when markets or communities move hourly. The system also had to tolerate noisy public sources, avoid overfitting to a single site, and still return something useful when queries were broad or ambiguous.
Scope & timeline
Directional highlights for this anonymized portfolio entry-useful for understanding depth of work, sequencing, and ownership.
Delivery window
7 weeks
Architecture
Two-agent pipeline
Orchestration
n8n workflows
Freshness priority
Live source pulls
Engagement note
The team executed in tightly defined milestones with weekly validation loops, keeping scope, quality, and rollout confidence aligned throughout delivery.
Weeks 1–2
Signal sourcing design
Defined topic templates, source mix, and freshness checks for Reddit and complementary web inputs.
Weeks 3–4
Acquisition agent
Implemented retrieval tuned for recency and relevance with failure handling for noisy queries.
Weeks 5–6
Summarization agent
Standardized briefing format, uncertainty labeling, and safe synthesis over volatile sources.
Week 7
Automation & handoff
Wired n8n schedules, alerts, and operator documentation for ongoing tuning.
Execution
Kenstin split responsibilities cleanly: acquisition agents optimize for recency and relevance; summarization agents optimize for clarity, structure, and safe generalization. That separation makes failures easier to diagnose (retrieval vs.
generation) and allows tuning without breaking the whole chain. We also defined explicit freshness thresholds and fallback behavior so outputs remained useful even when source volatility spiked.
Agent 1 focuses on live Reddit and complementary web retrieval tuned to the user’s topic. Agent 2 transforms raw findings into concise summaries with consistent formatting-highlighting what changed recently, what is consensus vs.
speculation, and what may need verification. The workflow includes retry policies, source sanity checks, and operator-friendly alerts to maintain confidence in daily automation runs.
The pipeline reliably produced timely, scannable briefings from fast-moving sources, reducing manual monitoring time and improving situational awareness for teams that depend on up-to-the-minute context. Stakeholders could make faster decisions because updates arrived in a consistent format with clearer confidence signaling.
The stack is selected for reliability, maintainability, and production readiness.
Previous case study
Kenstin Technologies modernized an HR suite with AI-assisted screening, structured performance signals, and compensation analytics-so hiring moves faster and people decisions rest on consistent data, not gut feel alone.
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