Agents Built on Anthropic's Claude, When That's the Right Fit
We build agents on Anthropic's Claude models and tool-use framework for the tasks where they're a strong fit — and say so plainly when another provider suits your task better. This page is about how we evaluate and build, not a spec sheet for someone else's product.
A Model Choice, Made for Reasons Specific to Your Task
Clients who ask for Claude specifically usually have a reason rooted in their own stack or team — a document-heavy workflow, an existing internal tool built around it, or a preference for how it follows detailed instructions. We treat that as a starting point to evaluate, not a decision to accept unexamined.
Instruction & Boundary Adherence
We write the escalation rules and things-it-must-never-do list in plain language and test whether the agent actually holds to them under an awkward or adversarial input.
Tool Use Wired to Real Systems
The agent's available actions are scoped to exactly what the job needs, tested against real API responses — not a mocked demo environment.
Grounded in Your Own Material
Answers and decisions come from your documentation and records, reviewed against real cases before launch, not the model's general knowledge.
What We Actually Weigh, Vendor-Neutral
We don't rank providers in the abstract — strength shows up differently depending on the task. These are the categories worth evaluating for yourself, whichever provider you're comparing.
Tool-Use Reliability
How consistently the model calls the right tool with correctly formed input, especially on a long or multi-step request.
Context Handling
How well the agent stays accurate and grounded when it's working from a long document or a lengthy conversation history.
Cost Per Call
Priced against your expected call volume and typical input length, not a headline rate.
Boundary Adherence
How reliably the model respects the escalation rules and hard limits we set, particularly under an ambiguous or edge-case input.
We evaluate these against your specific task during scoping and tell you plainly where the trade-offs sit — not which provider we'd rank first in general, because there isn't a single honest answer to that question.
The Model Is One Layer, Not the Whole Agent
The model generates a decision. Everything around it — what data it's allowed to see, which actions it can call, what triggers a human handoff, what gets logged for review — is engineering work we do regardless of which provider sits underneath. That layer is what determines whether an agent is trustworthy in production, not which model wrote the underlying text.
We design the decision boundaries and integration layer to be provider-agnostic where we can, so a future change in pricing, availability or capability at Anthropic doesn't mean rebuilding the agent from scratch.
See Our Engineering ApproachWe Test Against
Realistic inputs, ambiguous requests, and the edge cases most likely to trip a tool call or a boundary before launch, not just the happy path.
We Monitor For
Failed or malformed tool calls, unexpected behaviour after a provider-side update, and drift in answer quality over time.
The Same Agent, Different Underlying Model
The job the agent does — support, sales, document processing, workflow — and the model it runs on are separate decisions. Start from the job.
OpenAI Agent Development
The comparable approach built on OpenAI's models and tooling.
LangGraph Agent Development
For multi-step workflows better expressed as an explicit graph of steps and decisions.
AI Agent Engineering
How we approach evaluation, testing and monitoring, independent of the model choice.
Claude Agent Questions
Are you an official Anthropic partner?
We don't claim any formal partner or certification status. We build with Anthropic's publicly available Claude models and tooling the way any development team can, and we'd rather you judge a build on what it does than a badge on this page.
Is Claude always your recommended model?
No — we evaluate against the task, and different tasks favour different providers. See OpenAI Agent Development for the other side of that comparison. What we will say plainly is which categories of strength matter for your specific build, and why.
Do you commit to a specific Claude model version for a project?
We name the model family in scope and document what we tested against, but we don't promise a frozen version indefinitely. Providers update and retire models on their own schedule, and a version change gets evaluated before an agent is moved onto it — not applied silently.
Why would a client specifically ask for Claude?
Most often because of how the client's own team, or an existing internal tool, already works — not because of a number on a benchmark chart. We're honest that benchmark claims go stale fast and evaluate current behaviour against your task directly rather than quoting a published score.
What do you actually evaluate before choosing a model for a build?
Tool-use reliability on the specific actions the agent needs to take, how it handles the amount of context your task requires, how carefully it follows the boundaries and instructions we set, and cost per call at your expected volume. We share that reasoning in scoping rather than asserting a winner up front.
Tell Us What the Agent Needs to Do
We'll tell you plainly whether Claude is the right fit for it, or whether another approach serves the task better.