AI agents mark a departure from traditional software. Where standard workflow tools rely on deterministic logic (predictable output given input), AI agents operate more flexibly, and sometimes non-deterministically. That flexibility helps generate novel solutions, but it also needs controls, especially in financial services.
What AI agents do differently
Reasoning
Language-based reasoning
AI agents interpret and act on everyday language, rather than following static rules or scripts.
Adaptation
Adaptive decision-making
They consider past outcomes and emerging information to adjust tasks in real time, something rigid workflows often cannot achieve.
Proactive problem-solving
Beyond automation, they can spot patterns and suggest next steps, providing near-real-time analysis and insights.
Tool use for real-world tasks
By linking to external systems (CRMs, finance platforms, or communication tools) agents can retrieve data, generate invoices, or dispatch notifications across your digital ecosystem.
Flexible workflow patterns
Some tasks need a “react” pattern, handling each step as new data arrives. Others benefit from a “critic” pattern, adding self-checks before moving forward. That variety balances speed and quality control.
Control
Human in the loop
While agents can work autonomously, they are designed to pause for human oversight at critical junctures. That combines AI efficiency with human judgment, so key decisions get proper validation.
Why they differ from BPM and most SaaS
Fluid vs fixed processes
Traditional workflow or BPM tools excel at routine procedures. AI agents adapt to changing demands, interpreting tasks on the fly and cutting the time spent revising rigid workflows.
Context-driven interactions
Conventional software often needs reconfiguration or extra code whenever requirements shift. AI agents adjust through language-based reasoning, reducing downtime and manual effort.
Seamless growth
Many SaaS platforms focus on narrow sets of functions. AI agents can integrate across multiple systems and scale with new use cases without extensive overhauls.
Memory and learning
Recent work explores episodic memory, a step towards modelling aspects of human cognition (see research paper). That helps agents recall and apply past interactions more accurately.
Why they matter for your business
Reduced manual overheads
Agents take on repetitive tasks, freeing teams to focus on higher-value work.
Intelligent decision support
By consolidating data from various sources, agents deliver insights quickly and help guide informed decisions.
Long-term scalability
As your organisation evolves, agents adapt without forcing you to rebuild key processes or invest in multiple siloed tools.
Looking ahead
When deployed effectively, AI agents can transform daily operations: managing complex tasks, responding as priorities shift, and learning from past outcomes. At Fluid Tech, we help financial services organisations identify where agents add value, put them into production with the right controls, and keep them reliable over time. H/T to Building Effective Agents by Anthropic, more technical and worth a read. anthropic.com.