The build vs buy AI agent question in life sciences BD now arrives in the first ten minutes of almost every call. It is usually phrased like this: "How is this better than me going into Claude and creating an agent to do this search for me?"
It is a fair question and it deserves a straight answer. Build the agent if you have a data team and a warehouse. Buy if your BD team is five people and none of them own a pipeline. The honest version of the answer is that the weekend agent is real, it works, and it stops working about six weeks later for reasons that have nothing to do with the model.
Here is what actually separates the two.
What an agent on ClinicalTrials.gov genuinely can do in a weekend
Let us be specific, because vendors are cagey here and it costs them credibility.
ClinicalTrials.gov has a clean public API. No key, no contract, generous rate limits, structured JSON. An engineer who has never touched it can have a working query in under an hour. Point a capable model at that API and give it the ability to call it, and by Sunday evening you have something genuinely useful:
- Pull every interventional study registered in the last 30 days, filtered by phase, condition, and sponsor class.
- Ask questions in plain language over the result set. "Which phase II oncology studies registered this month list a sponsor I have never worked with?" The model reads the JSON and answers.
- Summarise a protocol. Eligibility criteria, endpoints, arm count, site count, estimated enrollment. Models are good at this and the registry text is well structured.
- Draft a first-pass outreach angle from the study record. Not a great one, but a real one.
- Run the whole thing on a cron and drop the output into Slack.
That is a legitimate capability and anyone telling you otherwise is selling something. If your ask is "show me new registrations in my therapeutic area every Monday," build it. You will be done this weekend and you will own it.
What it cannot do
The gap is not intelligence. The model is fine. The gap is everything the registry does not contain and everything that happens between Monday and Monday.
Entity resolution across sponsor aliases and subsidiaries. This is the one that quietly ruins the project. The registry sponsor field is free text entered by whoever filed the record. The same company appears as "Genentech, Inc.", "Genentech", "Roche", "Hoffmann-La Roche", and "F. Hoffmann-La Roche Ltd" across different studies, and those are not always the same commercial relationship. Subsidiaries file under their own names. Acquired biotechs keep filing under the acquired name for years. Your agent will treat each string as a separate account, your pipeline will show forty sponsors where there are twelve, and your BD rep will pitch a company that is already a client under a different legal entity. Resolving this is not a prompt. It is a maintained mapping table with a human adjudicating the ambiguous cases.
Change history over time. This is the subtle one. A registry query returns today's snapshot. It does not tell you that this study moved from "not yet recruiting" to "recruiting" last Tuesday, that the sponsor added three countries in August, or that the principal investigator changed. Those transitions are the signal. The snapshot is not. To see change you must store every snapshot and diff them, which means you now run a database, a scheduler, and a backfill, and you have to have started months before you needed the answer.
Commercial contacts. The registry gives you a study contact, which is frequently a coordinator inbox or a site nurse. It does not give you the VP of Clinical Operations, the Head of Outsourcing, or whoever actually signs a CRO contract. That data lives in an entirely different class of source and your agent has no path to it.
Alerting while you sleep. A cron job that runs a query is not monitoring. Monitoring means watching a defined set of accounts across registry activity, hiring, financing, and news, deduplicating what you already told the rep, and deciding what crosses the threshold to interrupt someone. Every one of those is a system, not a prompt.
The maintenance bill
Nobody quotes this part, so here it is.
| What breaks | How often | Who fixes it |
|---|---|---|
| Registry schema or field changes | A few times a year, unannounced | Your engineer, reactively, after output goes blank |
| Sponsor alias drift (renames, M&A, new subsidiaries) | Continuously | A human adjudicating ambiguous matches |
| Snapshot storage and diffing | Daily, forever | Whoever owns the cron and the database |
| Model behaviour changes on version upgrade | Every few months | Whoever wrote the prompts, re-testing |
| Contact data staleness | Constantly | Requires a separate data source entirely |
| The person who built it leaves | Once | Nobody, and that is the real failure mode |
That last row is the one to take seriously. Internal tools built by one motivated person have a half-life measured in that person's tenure. When they move teams, the agent keeps running and quietly degrades, and nobody notices until a rep pitches a study that closed enrollment four months ago.
The weekend build is cheap. The Tuesday-in-March maintenance is not.
When building is the right call
Sometimes it is clearly right, and you should not let a vendor talk you out of it.
Build if you have a data team and a warehouse. If registry data can land next to your CRM, your delivery history, and your feasibility data in the same query engine, you can answer questions no vendor can: which sponsors look like the ones where you win, which indications match your site network, which studies your competitors are already running. That is a genuine advantage and it is yours.
Build if your scope is narrow and stable. One therapeutic area, one region, one question asked the same way every week. Narrow scope means fewer edge cases and a maintenance bill you can actually carry.
Buy if your BD team is small and the tool has no owner. Buy if you need commercial contacts. Buy if you need the change history and you have not been storing snapshots since last year, because you cannot backfill a timeline you never recorded.
The results people get from consolidating this work are the reason the question matters at all. Frontify cut research time by 90%. Cytel went from five research sources down to one and reduced research time by 50%, with account planning running 30% faster. Six hours back per rep per week at Cytel. None of that came from a better model. It came from not maintaining six brittle pipelines.
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Using both, via the API and MCP
The framing of build versus buy is usually wrong anyway. The better question is which layer you own.
Own the layer that is specific to you: your scoring, your territory logic, your fit model, your feasibility data. Buy the layer that is expensive to maintain and identical for everyone: entity resolution, change history, contacts, monitoring.
Then connect them. Salesmotion exposes an API, and an MCP server that lets Claude call the underlying account intelligence directly as a tool. So the agent you were going to build this weekend still gets built - it just calls a maintained data layer instead of scraping a registry and guessing at aliases. Your engineer keeps writing the logic that is actually yours. Nobody on your team owns the sponsor alias table.
That is the version that survives the builder leaving.
Frequently asked questions
Can I just use the ClinicalTrials.gov API directly and skip all of this?
Yes, for new registrations in a defined area. It is a genuinely good public API and you should use it. The limits show up when you need to know what changed rather than what exists, when the same sponsor appears under five names, and when you need to reach a commercial decision maker rather than a study coordinator.
How long does a weekend agent actually last before it degrades?
In the teams we have watched, output quality starts slipping within a couple of months, usually from sponsor name drift rather than anything dramatic. The agent keeps producing confident output the whole time, which is what makes it dangerous. Degradation is silent.
How does search work inside Salesmotion?
You work from the sponsor rather than the study. Find the company by name or domain, or build the set with the therapeutic area, phase and modality filters, then work from the account, where the trials, signals, contacts and brief all sit together.
We have a data team. Does that settle it?
It settles the build question for the analytics layer, not the data layer. A warehouse team can build excellent scoring on top of registry data. What they generally do not want to own is a maintained sponsor alias mapping and a contact database, because neither is a differentiator and both need continuous human attention.
If you want to see what the bought layer covers before you scope the build, the CRO business development overview walks through what is tracked and what is not, and the MCP documentation shows how to call it from an agent you write yourself. Bring the agent you already built - the useful conversation is about which half of it you should stop maintaining.
