Short answer
To deploy your first AI agent, pick one workflow and start with read-only access. Write the memory structure and behavior rules before launch, add systems one at a time, and budget a few hours a week for tuning. Our own deployment took about a week to launch and about three more weeks before we trusted it with real work. Lattice Partners is an AI implementation partner based in Los Angeles and New York that helps companies build an AI strategy, trains their teams on AI tools, and builds custom agents and software.
- Fewer integrations at the start make problems easier to find.
- Memory and access rules take more work than the model does.
- Plan for review on anything with numbers or anything external.
What are the steps to deploy a first AI agent?
There are seven steps, from choosing a workflow to measuring the result. The table gives the order and what each step produces.
| Step | What you do | What you have at the end |
|---|---|---|
| 1. Pick one workflow | Choose a repeatable task with a clear owner | A one-sentence job description for the agent |
| 2. Set access tiers | Read-only first, then internal writes, then gated external actions | A written access list per system |
| 3. Design memory | Decide what the agent must remember and where it's stored | A file structure for people, projects, and decisions |
| 4. Write the rules | Define tone, silence rules, and security limits | A behavior document the agent reads every session |
| 5. Connect one system | Add a single integration and watch it work | A tested first connection |
| 6. Add review | Put a human check on numbers and external messages | A named reviewer and a log |
| 7. Tune weekly | Adjust rules, memory, and access from real use | A shorter list of fixes each week |
Which workflow should be the first one?
Pick a workflow that repeats every week, has a clear owner, and doesn't send anything outside the company at first. Good first choices are meeting summaries with action items, syncing data between two systems, and monitoring a project board for blockers.
The flashy use case is rarely the best one. For us, the real value came from mundane work, such as summaries, data syncs, and board monitoring. What Does an AI Agent for Operations Do? lists these tasks in a table.
How should you set the agent's access?
Set access in tiers and expand slowly. The agent reads freely, writes internally, and needs human approval for anything that leaves the company.
- Read-only first. Start with read access in every system.
- Expand one system at a time. Add write access only after the agent behaves well in that system.
- Gate external actions. Emails, social posts, and client messages need explicit approval.
- Log everything external. Keep a record of each outbound action.
Our agent can send email from its own address but needs approval before sending from a person's address. The glossary explains autonomy level and human in the loop.
What should you decide about memory before launch?
Decide what the agent must remember, where each kind of fact lives, and how old entries get condensed. An agent starts each session with no memory of the last one, so memory is a design task.
We use a layered file system: daily logs for raw events, per-account files for context, and a long-term file for distilled insights. We designed ours on the fly and refactored twice, so we'd write the structure first next time. Plan to spend a large share of the architecture work on memory and context. The glossary has an entry on memory, and The Shared Brain covers a related idea.
What rules should the agent follow?
Write a behavior document that covers tone, where the agent stays silent, and security limits. Without explicit rules, an agent defaults to hedged, polite language and tries to help everywhere.
Useful rules from our deployment include:
- Stay silent in specific channels unless asked.
- Redact API keys from outbound messages.
- Never post financial data in group chats.
- Move files to a trash folder instead of deleting them, so mistakes are recoverable.
How should you review the agent's work?
Put a verification step on anything with specific numbers and on every external message. Trust follows a ratchet: it rises slowly with good work and drops fast after one visible error.
Our agent once put a made-up figure in a draft. We caught it in review, and now content with numbers always gets a check. Using AI Without Outsourcing Your Judgment explains how to keep review from becoming a rubber stamp.
How much time should you budget?
Budget about a week to deploy and about three weeks of tuning before you trust the agent with real work. After that, expect 2 to 3 hours per week of oversight, which should shrink over time. Model costs for our agent run a few hundred dollars per month.
For OpenClaw, the open-source framework we used, see How to Set Up an OpenClaw for Your Company.
How Lattice Partners does this
Lattice Partners builds custom AI agents for mid-market companies and runs one internally. In Learn, we pick the first workflow with you. In Tailor, we set the access tiers, memory structure, and rules.
In Build and Ship, we aim to have a first version running within a few weeks. Hands-on training covers the people who work with it. Iterate then tunes the agent from real use, and most products go through 2 to 3 iterations. From AI Strategy to Production shows how a plan becomes working software.
Custom software is a one-time build and the client owns the code. We don't charge a recurring per-seat or per-module license.
Frequently asked questions
How long does it take to deploy an AI agent?
Our first deployment took about a week to launch. It took about three more weeks of tuning before we trusted the agent with real work.
What access should a first AI agent have?
Start with read-only access. Add internal write access one system at a time, and require human approval for emails, posts, and any message that leaves the company.
What should an agent remember between sessions?
Anything it needs to act consistently: people, projects, decisions, preferences, and rules. Store each kind in its own file, and condense old entries on a schedule.
How much does it cost to run an AI agent?
Our model costs run a few hundred dollars per month. The bigger cost is 2 to 3 hours per week of human time for tuning and oversight.
Do you need a developer to deploy an AI agent?
You need someone comfortable with APIs and access controls to connect the systems. Many mid-market companies bring in a partner to build the first one and train the team to run it.
Related reading
About Lattice Partners
Lattice Partners is an AI implementation partner based in Los Angeles and New York. We help companies build an AI strategy, train their employees on AI tools like ChatGPT and Claude, and build custom AI agents and software. Clients own the code we build. Lattice Partners was founded by Colin McDonnell, Martin Anquetil, and Christian Yeghnazar. Contact us to talk about your use case.