Strong Technical Foundations with AI-powered autonomous agents
Inventory Existing Systems
It's unlikely there won't be any existing tools. There may be existing spreadsheets, databases, tools and processes. We'll need to understand which tools we can link together, which we shouldn't and how it will fail. Firstly, we'll want to understand what systems currently exist that are doing something useful. In a project involving ticketing systems, internal knowledge bases and approved business APIs, the architecture can't be designed as if these tools don't exist. We'll need to understand who owns the tools, are there limitations around integrating them and what data they hold. For example, during an investigation, the ticketing API may have a successful response but no update to the customer record. Whilst this is not a case study or specific to any business, it makes it easier to see what decisions are being made. Having clear ownership of existing tools makes it easier to maintain the technical foundations of your autonomous AI agents.
Take inventory of existing tools and who owns them. This should be an honest reflection of what's being used. It's likely the most impactful tool will be the one you use every day, even if it's a spreadsheet. Consider how the operations coordinator is handling information flow between ticketing systems, internal knowledge bases and approved business APIs. What is the source of truth? This ensures nothing gets missed in your new design. You should start your integration with diagrams showing how the existing tools will be used. Often when designing AI-powered autonomous agents, decisions around existing tools can mean the difference between a promising demo and a reliable system.
Source of Truth
Decide which application will be the source of truth for a field. Who can update it? How will it be updated? This will avoid issues where one API call can silently fail and cause issues in another system. Where there are common fields, ensure there's a clearly defined source of truth. Having clear ownership of data in your AI-powered autonomous agents makes it easier to maintain the technical foundations.
For example, if during an overnight event, an event was created twice. This means the team in the morning will have to resolve the duplication. It's worthwhile to decide upfront who owns what. For example, deciding who owns the field. Whilst there may be multiple systems holding the customer address, payment status or workflow status, it's normally best practice to have one source of truth. Decide who owns what and have this documented. Don't add any synchronization until you've decided the rules for when there's a conflict. Decide who owns every common field. Often when designing autonomous AI agents, decisions around data ownership can make the difference between a promising demo and a reliable system.
Consider Failure Scenarios
Some fields may be safe to auto-classify, such as a facilities request. However, others like a payment change may need approval. It's worth considering some scenarios upfront. Sometimes integrations can fail mid-way through a process. This could cause duplication, timeouts or temporary unavailability. Decide how you'll handle retries, prevent duplication and resolve conflicts whilst it's still easy to make changes to the workflow. Having clear ownership of timeouts and retries makes it easier to maintain the technical foundations of your AI-powered autonomous agents.
Sometimes distributed operations will fail. Decide how you'll retry. Timeouts don't tell you if the last action was successful. Decide how you'll handle idempotency, retries, resolution and safely investigate any ambiguous results. If someone sends a message to the customer with conflicting instructions, blindly retrying could cause duplication or conflicting records. Often reliability will depend on these decisions more than the actual integration code. Decide how you'll handle retries, prevent duplication and resolve conflicts. Often when designing autonomous AI agents, decisions around timeouts and retries can make the difference between a promising demo and a reliable system.
Security
Integrations are a security concern. Think about how you're handling credentials, scoping and logging. If you're creating an agent to review service requests and prepare the next action, decide which operations actually need write access. Don't give extra data access for ease of use. Whilst this is a realistic example, specialised agents may be used to manage scheduling and service delivery in the future. However, you still need to have scoping on the shared permissions. This is a hypothetical example. Having clear ownership of secure interfaces makes it easier to maintain the technical foundations of your AI-powered autonomous agents.
Ensure your integrations have scoping and sensible logging. Give the minimum level of access required. Ensure credentials are secure, data is validated and logging is useful but doesn't create another source of sensitive data. Decide which calls need to be able to update records and which can just read. This can significantly reduce the impact of a compromised integration. Never allow your secure connector to be an open door. Often when designing AI-powered autonomous agents, decisions around secure interfaces can make the difference between a promising demo and a reliable system.
End to End Testing
Whilst each endpoint may pass all tests, the customer journey may fail. Ensure you've tested the full journey from the first request, through ticketing systems, internal knowledge bases and approved business APIs to the final confirmation. Ensure you've considered error handling and rollback. A unit test can't guarantee a full business process will run. Having clear ownership of end to end testing makes it easier to maintain the technical foundations of your autonomous AI agents.
For example, if you have a service desk and the address needs to be confirmed before any account records are updated. It's worth considering some scenarios upfront. Ensure you've tested full scenarios including create, update, cancel, recovery and resolution. Consider longer running responses and unusual values. If a system returns a success but no changes are made in another application, people will feel falsely confident. Ensure your end to end checks reflect what the user will see. Often when designing autonomous AI agents, decisions around end to end testing can make the difference between a promising demo and a reliable system.
Ownership/Change Management
Imagine your coordinator finds 3 different request types requiring 3 different escalation rules. It's worth considering some decisions upfront. Systems will change. APIs will have restrictions, fields will be renamed and people will leave who knew why the integration had been created in the first place. Document your mapping, ownership, error handling and deployment steps so a future team know how to resolve any issues. Having clear ownership of change management makes it easier to maintain the technical foundations of your AI-powered autonomous agents.
Having good documentation is key if you don't have access to the original developer. Document how to restart a failed job, how to update credentials, how to update field mapping and how to find the source of an error. Assign these to specific roles. Good documentation will reduce reliance on the original developers. This will help a future team make changes to your agent to review service requests and prepare the next action safely. Document your integration decisions to make future changes safer. Often when designing AI-powered autonomous agents, decisions around change management can make the difference between a promising demo and a reliable system.
Purposeful Flexibility
Often the best architecture isn't the most complex. Use tools to handle the expected load, keep data secure and allow reasonable changes. If you can't explain why a component is being used then perhaps it shouldn't be. For example, if an output from a tool contains malicious instructions within a normal support document. Having clear ownership of long term architecture makes it easier to maintain the technical foundations of your AI-powered autonomous agents.
Keep things simple. Whilst your architecture should be flexible enough to handle expected needs, don't over complicate things. Don't over complicate your architecture with complex abstractions when a simple connector and clear ownership would do. However, don't compromise on traceability. The perfect balance is having something that your team can manage today but also something that can be expanded on by a future team. Complex components should have a business reason. A good architecture will allow you to make changes without forcing every team to rebuild the bridge. The aim is not to have complex technology but a reliable relationship between systems and users.
