For anyone considering building AI agents
Implementing and building AI agents: how to approach it
An agent that works for one day is a demo. An agent that's still running after three months needs a different approach than a clever prompt.
Discuss your use caseSix forms we often build
Workflow automation
A recurring process end-to-end: pulling data, processing it, and passing it to the next step. The agent does the click-work, the human decides.
Research and monitoring agents
Continuously scanning sources, news or systems, filtering out signals and summarising what matters.
Internal copilots
An assistant built on your own knowledge and data that answers questions, looks things up and drafts, so the team works faster with more overview.
Client-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.
Data and integration pipelines
Connecting systems together: cleaning data, enriching it and moving it cleanly from one system into another, reliably and repeatably.
The engineering underneath
Why our agents keep working.
Context engineering
What the agent sees at any given moment. The context window is finite, so we decide what goes in and what stays out.
Harness design
The machinery around the model: memory, tools, protocols, sandboxes and approval gates.
The loop
Gather, act, verify, and retry on failure. That's the difference between an API call and an agent that keeps working on its own.
Memory & knowledge graphs
The agent remembers what matters: facts, relationships and earlier steps, in vector and graph memory.
Evals & guardrails
Measuring whether it's correct, guarding boundaries and having a human step in where it counts.
How we build it
Weeks, not months
A working agent or PoC is up in weeks. The use case often comes out of discovery.
Human-in-the-loop as a starting point
So you can trust what comes out, even when it goes wrong.
Through to the working prototype
Large-scale production software we hand over to a development partner, clean and with the knowledge secured.
Not every process needs an agent
- Sometimes it's just a process problem. We say so honestly, even when the answer to your use case is 'no'.
We use what we build.
Want to go through the terms at your own pace? Browse the knowledge base
Frequently asked questions
01 What kind of AI agents do you build? +
Agents that do one concrete task, in your own context: workflow automation, research and monitoring agents, internal copilots, client assistants, document and report agents, and data pipelines.
02 How quickly is there something working? +
A working agent or PoC in weeks, not months. The use case often comes out of discovery; we start with value, not technology.
03 Do you build this yourselves too? +
Yes. Back From the Future runs its own fleet of 15+ autonomous agents across seven departments, around one shared context.
Got a use case in mind?
Tell us what currently happens manually. We'll tell you honestly whether an agent is the answer.