How to set goals for an AI employee (and actually get results)

October 2, 2026 · 6 min read

Vague goals produce vague results — from humans and AI alike. The difference is that a human will ask a clarifying question. An AI employee will start working on whatever it inferred. Here's how to write goals that get you what you actually want.

The goal quality problem

Most founders who try AI employees for the first time write goals the same way they'd write a Slack message to a contractor: "handle our blog," "keep leads warm," "fix the onboarding." These are descriptions of areas, not goals. An area has no finish line, no success signal, and no constraint. It's a job description, not a task.

A goal that a human contractor would silently translate into something workable will confuse or stall an AI employee — or, worse, produce confident output that misses what you needed. The fix is not a smarter AI. The fix is a better goal.

The three components of a workable goal

Every goal you give an AI employee should have three parts:

1. The outcome. What the world looks like when the work is complete. Not "work on SEO" but "publish a 700-word blog post targeting the keyword 'AI employee for small business' in the blog directory."
2. The done signal. The specific, verifiable thing that proves it's done. A URL. A sent email. A row in a spreadsheet. A deployed file. If you can't point to it, the done signal isn't defined.
3. The constraints. What the AI should not do without asking first. Spending above a threshold. Contacting external parties. Changing live production infrastructure. Publishing without review. Explicit constraints prevent the class of AI mistakes that come from being helpful in the wrong direction.

Bad goals vs. good goals

This has no outcome, no done signal, and no constraints. An AI employee working this goal could produce ten blog posts, rewrite the homepage, or research a competitor strategy — all of which are "content marketing." You'll get something; it probably won't be what you needed this week.

This has an outcome (a published file), a done signal (a file at a path with a specific word count and schema), and a constraint (no deploy without approval). The AI employee knows what to do, knows when to stop, and knows what to check with you about.

A five-step process for writing AI employee goals

1. Start with the lever, not the task. Ask: what business outcome does this move? "Publish a blog post" is a task. "Increase organic traffic to the AI employee landing page" is the lever. The lever keeps the goal grounded even if the AI finds a better execution path than the one you imagined.
2. Write the done state in one sentence. Describe what the completed work looks like. "A blog post file exists at apps/landing/blog/how-to-replace-freelancer-with-ai.html with Article JSON-LD and a word count between 700 and 1000." That sentence is your acceptance criteria.
3. List what you don't want touched. Every goal has blast radius. A marketing goal touches the website; state which parts are off-limits. A finance goal touches accounts; state which ones require your approval before action. Constraints are not distrust — they're precision.
4. Name the deadline or the trigger. "By Friday" is a deadline. "Before the next board meeting" is a trigger. Without one, work competes with everything else in the queue and the urgency is undefined. AI employees prioritize; give them signal.
5. Attach the relevant context, not the reasoning. An AI employee needs the context to make decisions (the target keyword, the brand voice doc, the pricing page URL). It does not need an explanation of why the task matters — that slows it down. Link to the doc; don't paste the doc into the goal.

The review loop

AI employees in Summon work asynchronously. They start a task, run until they hit a blocker or complete the work, then update the issue with what they did and what, if anything, you need to decide. Your job in the review loop is:

• Check the done signal: does the work match the acceptance criteria you wrote?
• Clear any blockers the AI flagged: a missing credential, a decision only you can make, an approval gate.
• Either close the issue (done) or write the next goal in sequence.

A well-written goal closes cleanly. A poorly written goal produces a handoff comment that says "I wasn't sure whether to…" — which means you'll spend time in the loop that the goal should have resolved upfront.

Goals by department: quick examples

Here's what workable goal-writing looks like across Summon's eight departments:

• Marketing: "Publish a 700-word blog post to /blog/ targeting 'AI CFO for startups.' Article JSON-LD required. Draft only — do not deploy."
• Finance: "Pull all Stripe transactions from September into a CSV in /reports/ and categorize each by revenue, refund, or fee. Do not move money or change any subscription."
• Sales: "Find 10 founders in the US who run bootstrapped SaaS companies with 1–5 employees on Apollo. Export to a CSV in /leads/. Do not contact them."
• Operations: "Write the onboarding checklist for a new Summon customer to /docs/onboarding-checklist.md. Cover the first 7 days. Under 500 words."
• Engineering: "Add an FAQ section to the homepage below the pricing block. Copy from /content/faq.json. Do not change any other section."
• Support: "Draft email responses to the three open support threads in the inbox. Draft only — do not send. Save as /drafts/support-YYYY-MM-DD.md."
• Legal: "Summarize the key obligations in the attached contract in plain English. Flag anything that limits our ability to change pricing. Do not advise on whether to sign."
• Design: "Write a creative brief for a homepage hero redesign. One page. Cover the positioning, the primary CTA, and what we want visitors to feel. Save to /briefs/homepage-hero.md."

What to do when a goal fails

When an AI employee closes a task and the output doesn't match what you needed, resist the urge to blame the AI. Read the goal you wrote and ask: was the done signal clear? Were the constraints explicit? Did I attach the right context?

In most cases, the failure mode is one of three things: (1) the acceptance criteria were ambiguous, (2) a key constraint was missing, or (3) the AI was missing context that changed the scope of the work. All three are fixable at the goal-writing stage.

The correction is to rewrite the goal — not to write a longer explanation of what went wrong. The next version of the goal should be the thing you wish you'd written the first time.

Frequently asked questions

What makes a good goal for an AI employee?

A good goal has a clear outcome (what done looks like), a measurable signal (how you'll know it's done), and explicit constraints (what the AI should not do without asking). Avoid open-ended goals like "improve marketing." Write "publish two blog posts targeting the keyword AI employee by Friday" instead.

How often should I update goals for my AI employee?

Review goals weekly. AI employees work fast — a goal that takes a human a month may close in days. Set a weekly rhythm: check what closed, reprioritize the queue, and write two or three new goals before the next week starts.

Can AI employees set their own goals?

Partly. In Summon, AI employees can create child tasks (sub-issues) under a goal you set, but the top-level goal should come from you. The founder still owns strategy; AI employees own execution.

What happens if an AI employee gets stuck on a goal?

A well-configured AI employee will mark the task blocked and name the specific obstacle — a missing API key, a decision only the founder can make, or a dependency on another department. Your job is to clear that blocker and let the work continue.