AI Agents
From idea to an agent that does the work.
We build AI agents that genuinely take work off your hands, from first use case to an agent running in production. So you gain capacity, without hiring more people.
What we build
Agents that take over a concrete task.
Not a generic chatbot, but an agent that does one thing really well, in your own context. Six shapes we often build:
Workflow automation
A recurring process end-to-end: fetch data, process it, and pass it to the next step. The agent does the click-work, the human decides.
Research and monitoring agents
Continuously scan sources, news or systems, filter out signals and summarise what matters. From market monitoring to internal alerts.
Internal copilots
An assistant on your own knowledge and data that answers, retrieves and drafts, so the team works faster with more overview.
Customer-facing assistants
Agents that handle requests or talk to customers, with clear boundaries and a human watching where it counts.
Document and report agents
From raw input, calls, data or forms, to a clean document, deck or summary. Exactly what we do ourselves after every conversation.
Data and integration pipelines
Wiring systems together: cleaning, enriching and moving data from one system into another, reliably and repeatably.
How we do it
From use case to something that runs.
Sharpen the use case
We start from a use case that proves value, not from the technology. Often it comes out of discovery.
Build
A working agent or PoC in weeks. Human-in-the-loop as the default, so you trust what comes out.
Into production
If it works, we put it live and keep it running, or hand it over cleanly to a development partner.
We do not build large production software ourselves; we do that together with a development partner. That way we build what proves value fastest, without pretending to be a full build shop.
The engineering underneath
Why our agents keep working.
An agent that runs for months takes more than a smart model. The reliability sits in the layer underneath, and that is where we are at home.
Context engineering
What the agent sees at any moment. The context window is finite, so we decide what goes in and what stays out, keeping its attention sharp.
Harness design
The machine around the model: memory, tools, protocols, sandboxes and approval gates. Same model, but an environment that makes it reliable.
The loop
Gather, act, verify, and retry on failure. That is the difference between an API call and an agent that works on its own.
Memory & knowledge graphs
The agent remembers what matters: facts, relationships and earlier steps, in vector and graph memory rather than one long prompt.
Evals & guardrails
Measuring whether it is right, holding boundaries and letting a human step in where it counts. So you know it keeps working, not just in the demo.
We wrote a series about it.
Six dossiers on how agents really work.
Proof
We eat our own dog food.
Back From the Future runs its own fleet of 15+ autonomous agents across seven departments around one shared context. They route our meetings, keep client files current, run the entire social loop, drive tasks and guard the systems. What we build for you, we use ourselves first.
Where it fits
The build end of the line.
See, understand, build. Adoption runs underneath it all, because that is the goal of everything. Agents are where it becomes tangible.
Out of discovery
Almost always the starting point. In discovery the use cases surface that are worth building.
See the methodYou already have the use case
Already know what should run? Then we build directly, without the detour.
Discuss your use caseAdoption keeps going
While it runs, your team keeps growing with it. Training and rhythm keep it alive.
See trainingHave a use case in mind?
Tell us what is manual today. We will tell you honestly whether an agent is the answer.