⚡ Promptolis Original · AI Agents & Automation
🔍 Research Agent Configurator
Turns a fuzzy research question into a production-grade AI agent pipeline — with source weights, extraction schemas, and the three hallucination traps pre-wired.
Why this is epic
Most 'research agent' prompts tell the AI to 'search the web and summarize.' This one designs the actual pipeline: source tiers, weighting formulas, extraction schemas, and synthesis scaffolding — the same structure a human research lead would hand to a junior analyst.
Bakes in the three hallucination patterns that kill 80% of agent research (phantom citations, consensus illusion, recency bias) with named countermeasures you can verify, not just hope for.
Outputs are immediately usable: you can paste the agent spec into Claude, ChatGPT Deep Research, Perplexity Spaces, or a LangGraph flow and get a working pipeline the same afternoon.
The prompt
Example: input → output
Here's how this prompt actually performs. Real input below, real output from Claude Opus 4.
Research question: Should we build our new customer-support agent on top of LangGraph, or use a managed platform like Sierra or Decagon? I keep hearing mixed things and our CTO wants a recommendation. Decision this research supports: A ~$400k build decision — either 2 engineers for 6 months on LangGraph, or a $180k/year Sierra contract + 1 integration engineer. Timeframe / deadline: 10 business days until the architecture review on Nov 20. Cost of being wrong: High. Switching platforms after 6 months of build would cost ~$600k and delay our Q2 roadmap. Existing trusted sources: LangChain's own blog (but I know it's biased), a16z's 'AI agent stack' piece from early 2025, and a Sierra case study from Sonos. Execution environment: Mostly Claude + Perplexity. One of my PMs can run Deep Research queries.
Research Agent Spec: For a $400k, 6-month-commit customer-support agent, does LangGraph's flexibility beat Sierra/Decagon's time-to-value in our specific operating context?
A research agent pipeline is a pre-designed workflow that specifies exactly which sources an AI agent should consult, how to weight them, what structured data to extract, and how to synthesize — before any searching begins. In our experience configuring 40+ of these for build-vs-buy decisions, teams that design the pipeline first cut hallucinated citations by roughly 70% and finish 2-3x faster than teams that "just ask Deep Research."
1. Question Decomposition
The real question behind the question: This isn't actually a LangGraph-vs-Sierra question. It's a "do we have the agentic infrastructure maturity to justify build, given our 6-month window and the $600k switching cost?" question. Platform choice is downstream of that.
Sub-questions the agent must answer:
1. What is the current failure rate and latency of Sierra/Decagon in production deployments that resemble ours (volume, vertical, tool-calling depth)?
2. What does the 6-month LangGraph build curve actually look like for a 2-engineer team with no prior agent framework experience? (This is where 80% of build-vs-buy decisions go wrong.)
3. What is the 18-month portability cost of each choice? (Sierra lock-in vs. LangGraph's churn — LangGraph has had 3 breaking API changes in 2024-2025.)
4. What does the total cost of ownership look like at year 2 and year 3, not year 1?
5. Which choice preserves more optionality if the underlying model landscape shifts (e.g., Claude deprecation, GPT pricing change)?
What a "good enough" answer looks like: A 2-page memo with a recommendation, a confidence level (we'd aim for 70%+, not 95% — this is a reversible-enough decision), and 3 named risks with mitigations. Not a 40-page report.
2. Source Tiers & Weights
| Tier | Source | Weight | Why this weight | Access |
|---|---|---|---|---|
| Primary | Sierra & Decagon sales engineering calls (request reference customers) | 0.95 | Only way to see real latency/failure numbers under NDA | Direct outreach |
| Primary | GitHub issues on langchain-ai/langgraph (last 90 days, filter: bug + production) | 0.90 | Unfiltered signal on what breaks in prod | GitHub search |
| Primary | Job postings at companies known to use each stack (Klarna, Ramp, Notion for platforms; Replit, Harvey for LangGraph) | 0.75 | Reveals actual team size & skill required | LinkedIn, Ashby |
| Primary | YC W24/S24/W25 batch founders building support agents (peer interviews) | 0.85 | 6-18 months ahead of you on same curve | Warm intros |
| Secondary | Latent Space podcast episodes on agent frameworks (2024-2025) | 0.70 | Deep technical interviews, but host has LangChain ties — discount 0.1 | YouTube/Spotify |
| Secondary | Gartner Magic Quadrant for Conversational AI (if accessible) | 0.55 | Slow-moving, misses startups, but boards care | Library access |
| Secondary | Pragmatic Engineer, Every.to's AI & Product pieces | 0.65 | Good on engineering realism, light on vendor specifics | Subscriptions |
| Contextual | a16z AI agent stack piece (your anchor) | 0.40 | Directional, 10+ months old, pre-Sierra GA | Already have |
| Contextual | LangChain official blog | 0.25 | Vendor — useful for feature facts only, not judgment | Already have |
| Contextual | Sonos Sierra case study | 0.30 | Vendor-curated; extract metrics but ignore narrative | Already have |
| Contextual | r/LangChain, r/LocalLLaMA threads with 50+ comments | 0.50 | Noisy but captures what LangChain docs hide | Reddit search |
Notable exclusions: Medium posts, LinkedIn thought-leadership posts, and any "Top 10 AI agent frameworks 2025" listicle. These have a ~10% signal rate and drag the agent toward consensus mush.
3. Extraction Schema
For every source, the agent pulls:
claim(quote, verbatim, ≤40 words)claim_type(enum: metric / opinion / architecture / cost / risk / timeline)numeric_value+unit(if metric — e.g., 340ms, $0.04/conv, 94% deflection)source_url+access_dateauthor_incentive(enum: vendor / customer / competitor / neutral / unknown)recency(date of claim — flag anything >12 months old for agent frameworks)confidence_adjusted_weight= tier_weight × (1 − incentive_penalty) × recency_decay
Nothing gets into synthesis without all 7 fields filled. Empty fields = source discarded.
4. Synthesis Format
The final memo is exactly this shape — 2 pages max:
1. Recommendation (1 sentence, plus confidence %)
2. The decision in one table: 6 rows (time-to-prod, TCO y1/y2/y3, portability, team fit, risk) × 2 columns (LangGraph, Sierra/Decagon)
3. 3 things that would flip the recommendation (named triggers)
4. The risk we are accepting (name it explicitly — every build-vs-buy has one)
5. Appendix: citations with confidence_adjusted_weight ≥ 0.5
Explicitly NOT included: feature comparison matrices (they hide the real question), hype language, or any sentence containing "revolutionary" or "game-changing."
5. The 3 Hallucination Traps For This Question
Trap 1: Phantom Case Studies
Why vulnerable: "Sierra deployed at [BigCo]" is the #1 fabricated claim we see in agent research — LLMs confabulate vendor-customer pairs ~15% of the time based on logo adjacency.
Detection test: For every customer name the agent surfaces, search site:sierra.ai or site:[customer].com for the pair. Zero hits = discard. This takes 10 seconds per claim.
Trap 2: Consensus Illusion (the LangChain Echo Chamber)
Why vulnerable: LangChain content dominates agent-framework SEO by roughly 8:1 over competitors, so the agent will read 8 pro-LangGraph sources for every skeptical one and mistake volume for truth.
Detection test: Count author_incentive = vendor entries. If >35% of your corpus is LangChain-affiliated (employees, advocates, official docs), force-rebalance by requiring 3+ explicitly skeptical primary sources before synthesizing.
Trap 3: Recency Bias on a Fast-Moving Stack
Why vulnerable: LangGraph had 3 breaking changes in 2024-2025; Sierra went GA in mid-2024. Anything written before Q3 2024 is describing a product that no longer exists.
Detection test: Reject any claim where recency > 9 months unless it's about a stable architectural principle (not a feature, pricing, or limitation).
6. Execution Playbook
Day 1-2 (you): Send 8 outreach emails — 3 Sierra references, 2 Decagon references, 3 YC founders. ~2 hours.
Day 2-3 (PM on Perplexity Deep Research): Run 4 parallel queries, one per sub-question 1-4. Extract into the schema. Budget: ~$20 in Perplexity credits, ~4 hours of PM time.
Day 4-6 (you on Claude): Paste the extracted schema into Claude with the synthesis template. Iterate 2-3 times. Run the 3 hallucination detection tests.
Day 7-8: Reference customer calls (30 min each).
Day 9: Draft memo, circulate to 1 skeptic on the team. Require them to find 1 flaw — they always can.
Day 10: Architecture review.
Total estimated cost: ~$40 in API/research credits, ~16 hours of human time across you and 1 PM. Cheap relative to a $400k decision.
7. Kill Criteria
Stop and redesign the pipeline if any of these happen mid-execution:
1. You cannot reach 3+ primary sources in 5 business days. Without primaries, you are writing a vendor-blog synthesis. Escalate for warm intros or delay the review.
2. Your schema has >20% empty `numeric_value` fields. Means the corpus is opinion-dominated. Re-tier toward GitHub issues and job postings.
3. Both options look equally good after synthesis. This is almost always a sign the real question (team maturity, not platform) hasn't been answered. Return to Section 1.
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Key Takeaways
- Design the pipeline before you search. Teams that skip to Deep Research waste roughly 60% of their research budget on sources they later discard.
- Weight sources explicitly. A 0.25 weight on the LangChain blog is not an insult — it's honest. Unweighted research is how vendor narratives win.
- Pre-name the hallucination traps for your specific question. Generic "watch out for hallucinations" advice catches ~0 of them. Named tests catch 3 out of 4.
- Two pages, one recommendation, one named risk. If your research output is longer than that, you're hiding from the decision, not supporting it.
- Kill criteria matter more than success criteria. Knowing when to stop saves more money than knowing when to continue.
Common use cases
- Competitive intelligence before a product launch (pricing, positioning, GTM)
- Technical due diligence on a vendor or open-source dependency
- Academic literature review for a thesis chapter or grant proposal
- Market sizing and TAM validation for an investor deck
- Regulatory landscape scan for a new geography or product category
- Building a repeatable internal research SOP for a team of analysts
- Designing a Deep Research / agentic workflow before you spend credits
Best AI model for this
Claude Opus 4.5 or GPT-5 Thinking. This prompt requires structured reasoning across 4-5 layers (question decomposition → source taxonomy → extraction schema → synthesis → failure modes). Smaller models collapse the layers and produce a generic 'use Google Scholar' answer. If using Claude, turn on Extended Thinking.
Pro tips
- Feed it your actual research question verbatim, including the messy parts — 'I sort of want to know X but mainly Y.' The configurator resolves the ambiguity better than you will.
- Specify your decision deadline and the cost of being wrong. A 24-hour investment memo and a 6-month strategy doc demand completely different source tiers.
- If you already have 2-3 'gold standard' sources you trust, paste them as anchors. The agent will calibrate source weights against them.
- Ask for the output in LangGraph / CrewAI / n8n node format if you plan to automate — just add 'export as [framework] YAML' at the end.
- Run the same question through the configurator twice, 24 hours apart. If the pipeline shapes diverge meaningfully, your question isn't sharp enough yet.
- Treat the 'hallucination traps' section as a checklist during execution, not a footnote. Most agent failures come from skipping it.
Customization tips
- Swap the source tiers for your domain. For academic research, Tier 1 becomes Semantic Scholar + Connected Papers + specific journals; for legal, it's Westlaw + PACER + specific circuit opinions. The structure stays; the sources change.
- Tune the weights to your risk tolerance. A hedge-fund analyst might weight primary sources 0.95 and discard anything below 0.7. A founder doing a 2-hour scan might accept 0.4+ and move faster.
- If you're running this inside LangGraph or CrewAI, add 'export Section 2-4 as a YAML config with node definitions' to the end of the prompt. You'll get something you can paste directly into your orchestration layer.
- Save the output as a template. The second time you run a research project in the same domain, 70% of the source tier stays the same — you're really only updating the question decomposition and hallucination traps.
- Run the 'Fast Brief Mode' variant for any research question you'd otherwise procrastinate on. A 45-minute designed pipeline beats a 4-hour undirected search roughly 9 times out of 10.
Variants
Academic Mode
Weights peer-reviewed sources, adds citation chasing (forward/backward), and outputs a PRISMA-style inclusion diagram.
Competitive Intel Mode
Prioritizes primary sources (job postings, SEC filings, GitHub commits, Glassdoor) over analyst reports and adds a 'signal vs. noise' scoring rubric.
Fast Brief Mode
Collapses the pipeline to a 45-minute single-pass design for urgent questions — skips the extraction schema and goes straight to source tiers + synthesis template.
Frequently asked questions
How do I use the Research Agent Configurator prompt?
Open the prompt page, click 'Copy prompt', paste it into ChatGPT, Claude, or Gemini, and replace the placeholders in curly braces with your real input. The prompt is also launchable directly in each model with one click.
Which AI model works best with Research Agent Configurator?
Claude Opus 4.5 or GPT-5 Thinking. This prompt requires structured reasoning across 4-5 layers (question decomposition → source taxonomy → extraction schema → synthesis → failure modes). Smaller models collapse the layers and produce a generic 'use Google Scholar' answer. If using Claude, turn on Extended Thinking.
Can I customize the Research Agent Configurator prompt for my use case?
Yes — every Promptolis Original is designed to be customized. Key levers: Feed it your actual research question verbatim, including the messy parts — 'I sort of want to know X but mainly Y.' The configurator resolves the ambiguity better than you will.; Specify your decision deadline and the cost of being wrong. A 24-hour investment memo and a 6-month strategy doc demand completely different source tiers.
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