Task agents
Complete multi-step tasks across systems and report outcomes.
AI agents
AI agent development
Agents that receive an objective, work out the steps, use your systems and report back — built with defined permissions, real tool access and human oversight where it matters.
Direct answer
What is an AI agent?
An AI agent is software that receives a goal, determines the steps required, uses the tools and data it has been given, and completes the task — adapting when circumstances differ from the expected pattern.
Unlike a chatbot, an agent takes action in real systems. Unlike a script, it is not restricted to a predetermined sequence.
PROMTURE builds agents grounded in company data, connected to existing platforms, with defined permissions, logging and escalation.
The operational boundary
Where the current process stops working.
The useful definition of an agent is practical: can it do the work in your systems without someone driving every step?
That requires the right information, controlled tool access, deliberate uncertainty handling and observability. We build for all four.
Problems addressed
The work this system is built to remove.
01
Work requiring judgement that varies case by case.
02
Knowledge locked in documents nobody has time to search.
03
Processes stalled while someone looks up information and acts.
04
Customer interactions needing context from several systems.
05
Internal requests handled inconsistently.
06
Rule-based automation too brittle for real variation.
Capabilities
The system, broken into working parts.
Internal assistants
Answer from company knowledge with citations and permissions.
Customer-facing agents
Handle enquiries, take actions and escalate cleanly.
Retrieval systems
Ground responses in documents, policies and records.
Tool integration
Controlled access to CRM, databases, commerce and APIs.
Guardrails and evaluation
Action limits, approvals, logs and behavioural test suites.
Execution model
How the system moves from input to outcome.
01Objective received
02Context retrieved
03Plan formed
04Tools used
05Result verified
06Reported or escalated
Decision guide
Choose the simpler system when it can do the job.
| Dimension | Workflow automation | AI agents |
|---|---|---|
| Instruction | Fixed sequence | Objective; steps determined at runtime |
| Best for | Predictable high-volume tasks | Variable tasks requiring interpretation |
| Unexpected input | Fails or stops | Adapts or escalates |
| Reliability | Deterministic | Probabilistic; needs evaluation |
| Execution cost | Low and predictable | Higher; scales with model use |
Use cases
Where this is useful in practice.
01
Support cases resolved end to end
02
Sales research and CRM preparation
03
Operations monitoring and corrective action
04
Research gathered from defined sources
05
Employee onboarding across systems
06
Internal policy and process assistance
Frequently asked
Questions to resolve before building.
How is an agent different from a chatbot?01
A chatbot responds with text. An agent takes action in real systems. Many useful agents have no chat interface.
Can an agent access internal data securely?02
Yes. Access is scoped to specific systems and records, with authentication, permission boundaries and logging.
What happens when an agent is wrong?03
Confidence thresholds, approval gates and escalation paths control consequential actions. Every action is logged.
Do we need an agent or automation?04
If work is identical every time, deterministic automation is cheaper and more reliable. Agents fit variable work requiring judgement.
Which models do you use?05
Models are selected by accuracy, latency, cost and data handling. The architecture keeps the model layer replaceable.
Start with one process
Discuss ai agent development for your operation.
We will tell you what is worth building, what should be automated, and what is not.