Custom AI agents
A useful AI agent handles a bounded piece of work—researching, sorting, drafting, checking or updating—then hands the result to a person where judgment, approval or accountability belongs.
The commercial problem
Teams often have capable people spending time on the same early-stage research, classification, drafting or system update. The opportunity is not to hand over judgment blindly. It is to identify a well-defined task where a system can prepare, structure or progress work under clear rules.
Celerity designs agents around the actual operating route: what information they can use, what action they may take, how outputs are checked and when they must stop and ask a person.
Research context
Microsoft’s findings describe surveyed users and leaders; they do not establish that an agent is right for every process.
Microsoft’s 2025 Work Trend Index reported that 46% of leaders surveyed said their companies were using agents to fully automate workflows or business processes for teams or functions.
Microsoft · Work Trend Index · 2025
Microsoft’s 2026 Work Trend Index reports this among surveyed knowledge workers who use AI at work across 10 markets, including the UK. It is self-reported and not a productivity guarantee.
Microsoft · Work Trend Index · 2026
What Celerity builds
The best use cases are specific, auditable and connected to a real human owner.
Specify the input, output, permissions, quality threshold and points where the agent must ask for help.
Connect the approved information and systems that let the agent do a defined job with appropriate limits.
Automate structured preparation work, not unbounded decisions with unclear accountability.
Give a person a clear review route, visible outputs and an exception path for uncertainty.
How it works
Illustrative only: permissions, data, approvals and the appropriate degree of autonomy are defined with the business.
A person, scheduled event or system change gives the agent a bounded piece of work with expected inputs.
It researches, classifies, compares, drafts or structures information within its explicitly designed scope.
The route checks required information and flags uncertainty, missing data or actions outside the agent’s authority.
The output reaches the appropriate human for review, refinement, approval or a deeper conversation.
Illustrative use cases
Examples of system patterns, not claims about existing Celerity clients.
Collect agreed background information, flag relevant signals and hand a structured brief to the account owner.
Read a submitted enquiry, identify its intent and propose the correct queue or next action for review.
Prepare status summaries, update drafts or structured records before a human checks and releases the work.
Illustrative system examples / every agent needs an explicit scope, permission model and human escalation route.
Connected systems
Agents are most useful where information, systems and human approval are connected deliberately.
Use approved documents, policies and records within defined access limits.
Prepare or update defined fields, tasks and research context for review.
Classify and route incoming work into an accountable queue.
Provide a clear approval or exception surface for human owners.
Show workloads, exceptions and the quality of the operating route.
What we would measure
The right measures show whether the agent prepares useful work and keeps humans in the right decisions.
Time from a defined task trigger to a review-ready output.
Whether the output contains the agreed context and correctly asks for help where it should.
Which cases need escalation and what that reveals about the task’s true boundaries.
FAQ
A chatbot usually answers a conversation. An agent can be designed to complete a defined multi-step task using approved tools, with clear limits and human oversight.
Potentially, but only within an agreed permission model. High-impact actions can be designed to require human review and approval.
There is no single switch. We reduce risk through narrow task design, approved sources, validation rules, confidence thresholds and a clear human exception route.
Usually a bounded, repeated task with known inputs, a checkable output and a person who already understands how to review the result.
Related solutions
Agents are stronger when the task, system connections and human review route are already clear.
Research & sources
Microsoft’s Work Trend Index is used to frame a developing model of human-agent work. It is not evidence of a specific Celerity outcome.
Explore an agent route
We can test whether it has the inputs, guardrails and human review route to become a useful agent job.