At Palantir's DevCon 7, the company's software engineers argued that the largest factor in AI agent performance is not the underlying model but the set of tools the agent can use.
DevCon 7 · Palantir
Tools may matter as much as the model in AI agent performance
Palantir engineers argued that the set of tools an agent can use shapes its speed and output quality, challenging the idea that gains come mainly from switching to a stronger foundation model.
From basic to advanced: the tool ladder
Demonstrated expansion of the toolset, in order of capability
STAGE 1
Read tools
Basic access to retrieve data
→
STAGE 2
Write tools
Ability to change data and records
→
STAGE 3
Code execution
Compute, transform, and check its own work
Two levers, magnitude not yet measured
The same underlying model can perform differently depending on its tools. The size of each effect was not included in the available material, so the columns below are placeholders, not measurements.
Stronger model
Magnitude not published
Richer toolset
Magnitude not published
The announcement references a mid-weight model paired with the right tools, but no figures were available to quantify the improvement.
The case for tool design
Tool design can change both task speed and result quality.
Code execution lets an agent verify its own output.
Gains can come without changing the underlying model.
Open concerns
More tools mean more surface area for errors.
Code execution raises security and runaway-action risks.
Trade-offs were not addressed in detail.
Implication for enterprise teams
Investment in tool infrastructure may pay off as much as paying for a larger model.
Tool interfaces
How tools are defined and exposed to the agent
Permissions
What each agent is allowed to read, write, or run
Execution environments
Sandboxes
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