AI Agent Development
An AI agent is a system that plans and executes multi-step tasks on its own, using your existing tools and data, with guardrails that keep it reliable in production rather than just impressive in a demo.
Overview
Agents are useful the moment a task has more than one step: look something up, decide what to do, take an action, and check the result. We build agents that do exactly that against your real tools and data, not a scripted demo.
The hard part of agent development is everything around the model: reliable tool integration, guardrails, fallback handling, and monitoring for when an agent gets something wrong. That is where most of our engineering time goes.
Engineering
Real Engineering Behind Every AI Agent Development Engagement
AI Agent Development work here follows the same disciplined process as everything else Fastly builds — discovery, architecture decisions made deliberately, and code written for your specific system, not adapted from a generic starter.
Business Problems We Solve
Repetitive, Multi-Step Tasks
Work that follows a clear process but still requires a person to execute every step by hand.
Fragmented Tools and Systems
Getting anything done means switching between several disconnected tools and manually moving data between them.
No Reliable Way to Automate It
Traditional scripts break the moment a task requires judgment, not just fixed rules.
Support and Ops Can’t Scale With Volume
Request volume grows faster than the team handling it, and hiring alone doesn’t close the gap.
Agent Capabilities
How the Agent Operates
Every capability below works together in one continuous exchange between the agent, your tools, and your team.
Multi-Step Task Execution
Agents that plan a sequence of actions and carry them out, not just answer a single question.
Tool & API Integration
Direct integration with the systems your team already uses, so the agent can actually take action.
Memory & Context Management
Agents that retain the right context across a task instead of starting from zero every step.
Guardrails & Approval Flows
Explicit limits on what an agent can do autonomously, with a human approval step wherever it matters.
Monitoring & Fallback Handling
Clear visibility when an agent fails or gets stuck, with a defined fallback instead of a silent error.
Custom Agent Orchestration
Multiple specialized agents coordinated for workflows too complex for a single generalist agent.
Delivery
Development Workflow
The same disciplined process behind every ai agent development engagement, from first conversation to production.
04 Steps
- 01
Discovery
Understanding your goals, users, and constraints before any code is written. Output: Technical Brief.
- 02
Strategy
Defining the technical approach, architecture, and roadmap for what gets built. Output: Architecture Plan.
- 03
Development
Building in focused iterations, with regular check-ins and working software at every stage. Output: Working Software.
- 04
Launch & Growth
Shipping to production, then monitoring, refining, and scaling based on real usage. Output: Production Release.
Stack
Technologies We Use
Benefits
Work That Doesn’t Wait for Business Hours
Agents execute tasks continuously, without waiting on a person to be available.
Reduced Operational Overhead
Fewer manual handoffs between tools means less coordination overhead for your team.
Consistent Output Quality
An agent follows the same process every time, without the variance of a manual handoff.
Frees Your Team for Higher-Value Work
Routine execution moves to the agent, leaving your team for the judgment calls that actually need them.
Industries Served
Where ai agent development most commonly makes an impact.
- Finance
- Ecommerce
- Logistics
- Startups
Frequently Asked Questions
What is an AI agent?
An AI agent is a system that plans and executes multi-step tasks on its own — looking up information, making a decision, and taking an action through your existing tools — rather than only answering a single question.
Is an AI agent safe to run without supervision?
Agents are built with explicit guardrails and approval steps for anything consequential, so autonomy is scoped deliberately rather than open-ended.
What happens when an agent gets something wrong?
Monitoring and fallback handling are part of the build: failures are surfaced clearly and routed to a defined fallback, not left as a silent error.
Can an agent integrate with our existing tools?
Yes — agent development centers on direct integration with the tools and APIs your team already uses, rather than requiring you to switch systems.
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Delivery
Support Doesn't End At Launch
Every ai agent development engagement includes the Launch & Growth phase of our process — monitoring, refinement, and scaling support once the system is live, not a handoff the moment it ships.
Related Case Studies
Reference architectures and concept demonstrations touching this service.
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Ready to Talk About AI Agent Development?
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