Your AI agents get you about 80% of the way. Then they stall. The missing 20% lives in your team’s heads: judgment calls, shortcuts, exceptions. Dactic turns that, plus your existing systems, into one living context layer your agents can trust.

Context House is one continuously refreshed knowledge repository for your AI agents. One source of truth across your apps, so an answer today isn't based on last quarter's SOP.

Your AI agents don't know your rules. Yet. Watch one apply a client-specific rule it learned from the team, not from a prompt. dactic.io/?utm_source=x&utm_…

Agent Factory: create and deploy a specialized AI agent for any role, team or task, built on your own context layer, with one-click deployment. Fewer prompts to hand-tune, more agents that know the job.

Friday question: if your best operations person left tomorrow, what would your AI agents not know? Reply with the one thing. We read every answer.

What's the biggest reason your AI agents underperform? Missing company context, noisy inputs, untraceable spend, or stale knowledge? Reply with yours.

Context engineering, defined: giving an AI agent the right business knowledge, in the right format, at the right moment. A prompt is a sentence. Context is the whole workplace.

Why do enterprise AI agents stall at 80%? Tacit knowledge. Documents describe how work should happen. Your team runs on how it actually happens: judgment calls, exceptions, who to ask. Agents only see the first part.

AI agents don't fail because the model is weak. They fail because nobody told them how your business actually works: the unwritten rules, the exceptions, the "we always do it this way." That's the context gap.

Off-the-shelf AI agents get you about 80% of the way. The last 20% is the judgment calls and exceptions your team carries in their heads. Dactic captures that tacit knowledge and turns it into a living context layer agents can act on. See how it works: dactic.io/?utm_source=x&utm_…