Choose an AI agent that works with your GovCon team's approved model

Your IT team has approved a model endpoint. Your capture team has a folder of solicitation documents and needs a reliable requirements brief. The next decision is which application can turn those documents into useful work without creating a new subscription or moving the files somewhere they do not belong.

An agent's name alone does not answer that question. Choose it by testing the document task against the model and environment your organization actually supports. A useful first exercise starts with a public opportunity found in G2X: read the packet, identify the conditions that affect a bid decision, and produce a short brief a capture lead can check.

Start with the handoff your team needs

Consider a small proposal team reviewing an unfamiliar service requirement. The capture lead needs to decide whether to bring in the delivery manager. That manager needs the scope, staffing constraints, performance location and unanswered questions, with references to the original documents. A polished summary that misses an attachment creates more work for both of them.

This gives you a concrete way to compare agents. Can each one read the permitted files, follow the same instructions and leave a reviewable result? Can a colleague open the output and find the passages behind its conclusions? How much correction does it need before the delivery manager can use it?

Start the research with G2X's federal opportunity review workflow, then take the permitted public documents into your chosen workspace. You can do the search and manual review with a free G2X account. That research is useful whether your team uses an existing enterprise assistant, an agent connected to a managed model, or a model it operates itself.

Codex, Claude Code and Pi offer different connection choices

The software coordinating the work is often called an agent harness. It handles the conversation, files and tools around the model. Choosing a harness and choosing a model are related decisions, but their names need not match.

OpenAI documents custom model providers and local model connections for Codex. That provides options beyond signing in with a personal ChatGPT subscription. The proposed endpoint still needs to support the protocol and behavior the selected Codex configuration expects; a successful text response is only the first check.

Anthropic documents Claude Code through Amazon Bedrock, using AWS access to supported Claude models. That is a concrete path for organizations buying those models through AWS. It does not establish that an arbitrary open-weight model can replace Claude in the same setup.

Pi's upstream documentation describes built-in providers, local models and compatible endpoints. This can be useful when a team wants to evaluate different model families behind one working interface. The team still owns the work of confirming authentication, tool behavior and compatibility for its chosen endpoint.

These are connection options checked in September 2026, not a ranking of which agent writes the best proposal. Use the same public document packet to find out which combination works for your team.

Test the parts a chat demo skips

A short prompt can succeed while a full document task fails. Include an attachment with a relevant qualification condition, ask for exact page or section references, and check what happens when a file is missing. If the documents disagree, the output should identify the conflict and its sources rather than quietly choose one.

Keep the first test narrow. Reading a packet and drafting a brief does not require permission to send email, submit a response or change a CRM record. Give the reviewer a chance to inspect the proposed result before adding more actions.

Your service owner also needs to check where the rest of the work goes. Local transcripts, search requests, extensions and diagnostic services may have different destinations from model inference. For example, Anthropic's data-usage documentation describes distinctions across deployment options. Check the actual configuration rather than assuming that an approved model endpoint covers every connected tool.

Running software on a machine in GovCloud does not by itself approve that whole workflow. And a GCC subscription does not by itself specify where a separately installed agent runs. Our guides to Microsoft government environments and AI in AWS GovCloud cover those hosting decisions.

Choose the setup your team can repeat

For work authorized in G2X, Lumen, the GovCon Growth Agent, is another way to review documents in the context of your market research. Upload the permitted solicitation files and ask it to identify qualification gaps, cite requirements and help plan a response. The Lumen guide explains how to supply useful context. This is a separate service choice; it does not mean Lumen runs inside your organization's government cloud.

At the end of the comparison, keep the reviewed brief, the corrections it needed and the time spent checking it. Those tell you more than a confident first answer. Choose the setup that your colleagues can use consistently, with an owner who can support it, and teach the team that workflow. The skill you are building is sound GovCon judgment supported by AI, whichever approved model does the work.