Projetly AI Agents Are Live: What Each One Actually Does

Projetly's AI Agents went live this week. Before listing what they do, it's worth answering a harder question first: why should anyone believe an AI capability list at all, when most vendors are shipping one right now and most buyers have learned to discount them on sight.

That skepticism is measurable, not anecdotal. In G2's 2026 Buyer Behavior Report, internal resistance to AI adoption grew from 16% to 29% in a single year, even as 72% of buyers now call AI a must-have or a differentiator when evaluating software. Buyers want the capability and are getting warier of the pitch at the same time.

Why does AI transparency matter more than the feature list itself?

The same report found 87% of buyers prefer a transparent-AI vendor over a cheaper black-box alternative. Transparency is outweighing price on this specific dimension. That's the standard this post is trying to meet: explain what each agent actually does and why, rather than asserting a capability and moving on.

There's a second reason to be precise here. 49% of buyers had a CFO veto an already-approved purchase in the past year, and 75% expect positive ROI within 6 months. AI claims that can't survive a specific "what does this actually do" question are exactly the kind of thing a skeptical CFO vetoes after the fact. Vague claims cost deals later, not just credibility now.

What do Projetly's AI Agents actually do?

Projetly's AI Agents watch buyer and delivery signals across the deal-to-onboarding lifecycle, explain what they mean, and tell each team member the next best action with evidence, so revenue teams execute instead of investigate. That's the umbrella. Three agents matter most for the handoff and continuity thesis this blog has covered before:

• Deal Room AI builds the workspace from a plain-language opportunity description, and keeps deal context, memory, summary, and confidence current without manual entry, continuously, as the deal changes.

• Project AI carries full deal context into delivery once a deal closes, tracks milestones and blockers, and flags go-live risk before customers feel it. This is the same continuity mechanism this blog has argued for directly: the room that ran the sale keeps running into onboarding.

• NBA (Next Best Action) recommends a single, highest-impact next action per deal, backed by the evidence that triggered it, with the reasoning attached.

Each of these recommends and informs. None of them executes a transaction or takes an action a rep hasn't approved. That distinction matters given another finding in the same report: only 9% of buyers would let an AI agent actually execute a purchase, a large gap from the 60%+ who already use or plan to use AI agents for research and comparison. Buyers want agent-assisted evaluation, not agent-executed decisions, and that's the posture these agents are built around.

Why explain the reasoning instead of just listing the output?

An NBA recommendation without the evidence behind it is just another opinion competing with a rep's own judgment. The evidence is what makes it usable instead of ignorable. A rep who can see why the system flagged a stalled deal (three unanswered stakeholder messages, a milestone that slipped past its date) can act on that immediately. A rep who just sees "contact the buyer" with no reasoning has no way to judge whether the system caught something real or is pattern-matching on noise.

This same logic applies to Project AI's go-live risk flags. A flag with no evidence attached asks a delivery team to trust a black box during exactly the internal-resistance moment the buyer-behavior data describes. A flag that shows which milestone slipped and by how much gives the team something to verify or dispute. Showing the specific evidence behind a flag is what transparency actually means in practice.

A concrete example: what changes for a deal team this week

Picture a deal that closes today. Under the old flow, an AE fills out a handoff summary from memory, a CSM opens the account cold, and nobody notices a slipping onboarding milestone until the customer raises it. Under this week's launch, Deal Room AI has already kept the deal's context current through the sales cycle. Project AI inherits that context automatically at close, tracks the onboarding milestones against the timeline that was set, and surfaces a go-live risk flag the moment a milestone starts slipping, with the specific milestone and the specific evidence attached. NBA tells the account owner what to do about it, backed by that same evidence.

None of this replaces the account owner's judgment. It removes the gap between when a risk becomes visible in the data and when a person finds out about it.

Checklist: is an AI claim actually transparent?

• Evidence attached. Does every recommendation or flag show the specific signal that triggered it, not just the conclusion?

• Recommend, not execute. Does the agent tell a person what to do, or does it act without their approval? Buyer comfort data says the former is what's actually wanted right now.

• Survives a specific question. Can the claim hold up to "what does this actually do, concretely," or does it fall apart past the headline?

Most AI capability lists fail the first or third check, not because the underlying feature doesn't work, but because the description never had to justify itself against a skeptical reader. Internal resistance to AI adoption almost doubling in a year suggests that skeptical reader is now sitting in a lot of buying committees, not just showing up occasionally.

How does this fit Projetly's broader positioning?

Projetly's Digital Sales Room already carries deal context past closed-won into onboarding, the differentiator this blog keeps returning to. AI Agents extend that same continuity thesis into the how, not just the what: the same context that survives the handoff is now the evidence the agents reason from, not a separate AI layer bolted on top. See how that continuity problem shows up without AI agents involved at all in Digital Sales Room After Closed Won: Why Handoffs Break, and how it plays out specifically in the sales-to-CS handoff in Sales to CS Context Loss: Why the Handoff Format Fails.

Book a Projetly demo to see the AI Agents working on a real deal, or start a free trial.

FAQ

What are Projetly's AI Agents?

A set of agents that watch buyer and delivery signals across the deal-to-onboarding lifecycle, explain what the signals mean, and recommend the next best action with evidence attached, so revenue teams can act instead of having to investigate first.

Do Projetly's AI Agents execute actions automatically?

No. They recommend and inform. A person still approves and takes the action. This matches what most buyers actually want from AI agents right now: research and comparison assistance, not autonomous execution of a decision.

Why does AI transparency matter more than a longer feature list?

Because 87% of buyers in G2's 2026 Buyer Behavior Report say they prefer a transparent-AI vendor over a cheaper black-box one, and internal resistance to AI adoption nearly doubled in a year. A capability that can't explain its own reasoning is a harder internal sell than one that can, regardless of how sophisticated the underlying model is.

How does Project AI relate to the sales-to-CS handoff problem?

Project AI carries the same deal context that Deal Room AI kept current during the sales cycle directly into delivery, tracking milestones and flagging go-live risk before the customer feels it. It's the same continuity mechanism this blog has described in the handoff context, applied specifically to onboarding risk.

What does NBA (Next Best Action) actually recommend?

One recommendation per deal, the single highest-impact next action, backed by the specific evidence that triggered it, with the reasoning attached so a rep can judge whether to act on it immediately.