Data & AI
Agentic AI Development
AI agents that plan and carry out multi-step work across your tools, with clear permissions, full logs and a person approving what matters, built by a founder-led team.
Talk to the foundersWhat you get with Authect's agentic AI development
An AI agent doesn't just answer a question: it works towards a goal, deciding which steps to take, which tools to use and when it's done. That makes agents powerful for the multi-step tasks that eat up your team's time, and it means they need more care than a chatbot.
We build agents the way we'd build any system that acts on your behalf. The founders map the workflow with you, a senior engineer leads the build, and every agent gets narrow permissions, a full log of its actions, tests on real cases and a human approval step wherever a mistake would be costly.
We start small, with one workflow and a clear measure of success, then extend once the agent has earned your trust. The code, prompts and tool connections are yours from day one.
What we do
Types of AI agents we can develop for you
Different problems call for different kinds of agent. Many real systems combine several of them, with simple, predictable parts wherever possible.
Learning agents
Agents that improve from feedback: corrections from your team and the outcomes of past tasks shape how they handle the next one, with every change tested before it goes live.
Utility-based agents
Agents that weigh options against costs, risks and priorities to choose the best action, useful for scheduling, resource allocation and pricing decisions.
Goal-based agents
Agents given an objective, such as preparing a report or resolving a ticket, that plan the steps, use your tools and check their own work along the way.
Model-based agents
Agents that keep an up-to-date picture of a changing environment, such as stock levels or equipment status, and respond to what they observe.
Simple reflex agents
Fast, rule-driven agents that react instantly to specific signals, such as an alert or threshold, ideal for monitoring where predictability matters most.
Multi-agent systems
Several specialised agents coordinated by an orchestrator, each doing one job well, for processes too broad for a single agent to handle reliably.
Not sure which kind of agent fits your workflow? The founders can help you decide.
Contact usService benefits
Benefits of implementing AI agents
- 01
Multi-step work done faster
Agents gather information from several systems, fill in forms, draft responses and update records in one flow, so tasks that took hours of clicking take minutes of review.
- 02
Better, more consistent service
Every request is handled the same careful way, following your policies, with complex or sensitive cases routed to the right person straight away.
- 03
Work that continues around the clock
Agents keep processing requests overnight and across time zones, so customers in every market get a timely response and your team starts the day with a clear queue.
- 04
Capacity that scales with demand
When volumes spike, agents take on the extra load, so you can grow without your service levels slipping or costs rising in step.
- 05
Insight from every task
Because agents log what they do and why, you get a detailed view of where requests come from, where processes stall and what to improve next.
Industries
Industry applications of agentic AI
Healthcare
Monitoring device data and records for anomalies to flag to clinicians, coordinating schedules and resources, tracking medication stock and predicting supply needs, and lab workflow coordination, always with medical staff making the clinical decisions.
Retail
Watching stock levels and sales to propose orders, adjusting prices within rules you set, coordinating staff schedules, tracking supplier shipments and predicting delays, and managing loyalty and omnichannel follow-ups.
Media & Entertainment
Coordinating content distribution across platforms, tagging and organising media libraries, tracking licences and rights usage, and managing production schedules and resources.
Fintech
Monitoring transactions for unusual patterns, preparing compliance checks and reports, reconciling payments and supporting treasury and portfolio teams with analysis they review and approve.
Insurance
Checking claim documents, flagging possible fraud and settling straightforward claims within limits, preparing underwriting files, handling renewals and routing complex enquiries to the right specialist.
Logistics
Adjusting routes and delivery sequences as traffic and weather change, coordinating warehouse tasks, scheduling fleet maintenance, tracking shipments and starting contingency plans when delays appear.
Energy
Monitoring sensor data from sites and equipment, scheduling maintenance before failures, coordinating field crews and compiling the reports regulators and operators need.
Example system
An example of a multi-agent AI system
Here's how a multi-agent system could help a services business answer requests for proposals. It's an illustration of the design, not a client project: specialised agents do the groundwork, and people check and sign off every proposal.
Intake and privacy filter
Requests arrive by email, forms or a web portal. Team profiles and CVs pass through an anonymisation step that removes personal and confidential details before any model sees them.
Profile structuring agent
Request analysis agent
Matching and estimation agents
Proposal assembly and review
How we work
How we deliver agentic AI systems
Agents act on your behalf, so trust is built in steps: first they suggest, then they act with approval, and only then, where it's safe, on their own.
FAQs
Agentic AI FAQs
What is the agentic approach in AI?
It's a way of building AI systems that pursue a goal rather than respond to a single prompt. The system observes its situation, plans steps, uses tools such as search, databases or business applications, checks the result and decides what to do next.
What is an example of agentic AI?
An agent that handles a customer's request to change a booking: it reads the message, checks availability, applies your policy, updates the booking system, drafts a confirmation and asks a person to approve anything outside the rules.
What is the difference between generative AI and agentic AI?
Generative AI produces content, such as text or images, when asked. Agentic AI uses generative models as part of a larger loop: it plans, takes actions in other systems and works through several steps towards a goal.
How do you keep AI agents under control?
Each agent gets only the permissions it needs, every action is logged, high-impact actions need human approval, and agents are tested on real cases before launch and monitored afterwards. Autonomy grows step by step, based on results.
Where should we start with AI agents?
With one well-understood, repetitive workflow that spans a few systems and has a clear measure of success. We'll help you pick it in discovery, build a first version that suggests actions, then extend from there.