Agent Chaining: Unlocking Smarter Workflows with AI

Customers rarely come to us with just one problem. They might start with “help us extract text from images” or “detect quality issues”, but soon the scope widens: “Can it also check for damage? Track compliance? Flag anomalies?"

This is where agent chaining becomes exciting. Instead of building a separate system for every new need, the same infrastructure can be extended step by step, expanding from one solved problem into many more. For customers, it means lower costs, faster delivery, and solutions that grow with their needs. For us at Tiliter, it means building systems that are adaptable by design, not locked to a single use case.

Agent chaining makes this possible by breaking complex problems into smaller, specialised steps that can be executed in sequence. Instead of relying on a single model to handle everything, agents are linked so each performs the task it’s best suited for - keeping cost and performance under control.

Take text extraction (OCR). On its own, it pulls characters from an image. In practice, it’s often just the first step. A chained workflow might first run text extraction to capture numbers or letters, then only trigger a damage detection model in specific cases, say when certain product codes appear or when an object type is confirmed. This avoids unnecessary calls, reduces cost, and keeps decision logic transparent. At enterprise scale, those optimisations directly impact ROI.

The same principle applies just as well in Airbnbs, short-term rentals, or public facilities. A cleanliness evaluation agent checks whether a threshold has been met. If so, the next step assesses damages such as scratches, stains, or broken items. The workflow ensures the right checks happen in the right order, balancing accuracy with efficiency.

Why Agent Chaining Matters

1. Efficiency and Cost Control
   
   • Expensive agents only run when conditions are met.
   • This creates a transparent link between decision logic and cost.
2. Specialisation of Agents
   
   • Chaining avoids monolithic “do everything” models.
   • Specialised models are more accurate and easier to maintain.
3. Conditional Logic
   
   • Decisions cascade naturally.
   • Example: only perform a legal clause extraction if the first agent determines a document is a contract.
4. Scalability Across Domains
   
   • Enterprises can start with one use case and layer in more.
   • Infrastructure remains extensible rather than locked to a single function.

Beyond Static Systems

Legacy tools like barcode scanning or RFID tags still serve niche purposes but are static and built for narrow use cases and difficult to upgrade.

Agent chaining, in contrast, allows organisations to:

• Extend existing infrastructure with AI layers.
• Upgrade incrementally instead of replacing hardware.
• Future-proof systems as models improve.

In industries where capital investments are long-term but technology evolves rapidly, this flexibility is crucial.

The Role of LLMs

The rapid progress of large language models (LLMs) and computer vision makes chaining increasingly powerful. With every release, models improve in reasoning, classification, and multimodal understanding. This enables:

• Higher accuracy thresholds for chaining.
• Complex workflows automated end-to-end.
• Hybrid use cases that combine vision, language, and domain-specific logic.

Agent chaining provides a framework for adaptability. Workflows can swap in better models tomorrow without redesigning the entire system.

Hybrid Use Cases

Chaining is particularly relevant in hybrid workflows. For example, in receipt processing:

• A vision agent extracts line items.
• A language model categorises them into groups such as services, food, or utilities.
• A domain-specific agent summarises spending or flags anomalies.

The same logic applies in expense reporting, healthcare claims, or auditing. These emerging patterns highlight how specialised agents can be linked to deliver results beyond the reach of a single model.

Looking Ahead

Agent chaining makes adaptive AI systems practical. It connects specialised agents into sequences where each step adds value only when needed, keeping costs aligned with decisions and accuracy high. Unlike static workflows, chaining works directly at the model level, so enterprises can upgrade capabilities as stronger models emerge without redesigning the entire system.

For our customers, this means they don’t have to stop at solving one problem. They can start small and extend over time, knowing the same infrastructure can carry them further. For Tiliter, adaptability is the goal: building systems that don’t just solve today’s challenges but are ready for whatever comes next.