AI Agents vs Traditional Software: What's Really Changing and Where's the Value
In this article, I explain why agentic systems are becoming the foundation of a new generation of software and how they change development approaches. The material includes practical checklists and metrics.
Most applications operate by the rules of determinism: same input — same output and a strict execution path. The world has become more complex. Agentic systems work differently: they achieve goals in changing contexts, plan steps, choose tools, rely on memory, and operate within explicit policies. What's really changing, how an agent is structured, how to measure reliability and economics — and when agency provides measurable impact.
Key Shift: From "reaction to request" to "active goal achievement"
Agentic systems are truly a new class of software.
The classical model provided reliability. But where tasks spread into nuances — language, tone, exceptions, missing data — rigid scenarios break or become expensive.
Boundary of Differences: Four Key Shifts
1. Procedure → Goal
Fixed script versus optimization of result achievement
2. Routes → Planning
Static flow versus real-time plan reassembly
3. API Calls → Tools as Actions
Pre-wired methods versus contextual action selection
4. Transaction State → Memory & Context
Short/long-term memory for retaining meaning, not just operation status
Agent Architecture: What It's Made Of
- •Goal-setting: goal and success criteria
- •Planner: task decomposition, branching, parallelism, plan revision
- •Tools: managed access to external systems
- •Memory: short-term — session context, long-term — profiles, facts
- •Policies: allowed actions, limits, stop-lists, verification
- •Observability: decision tracing, metrics, explainability, auditability
- •Escalation: human-in-the-loop for risky and "gray" cases
In total, this is closer to a digital operator than a "form with backend": the agent works with the world, not just within a pre-written scenario.
Lifecycle: Two Different Worlds
Traditional Software
Input → Validation → Business Logic → Write → Response
Error → error code, retry if available
Agent
Goal → Plan Hypothesis → Tool Action → Result Observation → Correction → Repeat → Success Criteria
Failure → strategy change, request additional data, risk reassessment, escalation
This is where the effect appears: the agent closes the "edge" cases that previously settled on people and manual processing.
Reliability and Governance: Without This, Agents Lose Trust
Agents provide power, but without discipline they quickly turn into a "black box". You need:
- •SLAs: success rate, average steps to success, timeouts/aborts, escalation share
- •Decision tracing and explainability: record not just calls, but reasons
- •Minimum necessary permissions and command-guard before critical actions
- •Cost and latency control: model routing — LLM not always needed, caching, tool prewarming
- •Goal achievement tests: environment simulators, A/B policies, goal-oriented cases
Economics: Where Agents Pay Off
Agents are profitable when:
- • High variability of scenarios and many exceptions
- • Long interaction cycles
- • Context and memory are important
- • Tool orchestration is needed: KYC → scoring → offer → contract → invoice
- • Error cost is controllable through policies and escalation
Not profitable when:
- • Inputs are stable
- • Task is trivial
- • Regulators require strict determinism
Attempting to "make everything agentic" often loses to an honest "no."
Case Study from FinTech: "Soft Collection" Agent
Goal: Increase the share of payment promises without degrading NPS.
1. Goal-setting
For each debtor — obtain a payment date/amount promise
2. Plan
Call → confirm identity → clarify reason → offer options → record agreement → send SMS
3. Tools
Call, CRM, risk scoring, payment service, SMS templates
4. Memory
Refusal reasons, past promises, cultural norms, language
5. Policies
Stop-lists, repeat call limits, pressure prohibition, mandatory conflict escalation
6. Metrics
Promise rate, repeat contacts, average time to promise, NPS, contact cost
Traditional software would just "call by script." The agent hears context, changes tone, selects options, escalates disputed cases — and does this within the legal framework, with a decision trail and permission restrictions.
As part of the FynexAI project, we're working in this direction for soft-collection.
Anti-patterns of Agency
"LLM will solve everything"
Without tools and policies, an agent is just an expensive chat
Memory without policy
Risk of personal data leaks, hallucinations, and compliance issues
Black box
No decision tracing = no explainability and trust
Excessive permissions in production
Direct path to incidents
Conflicting goals
Unstable plans and quality degradation
Case Readiness Checklist for an Agent
Economics calculated: latency/cost, model routing, cache
Goal and success criteria formulated
Tools listed and defined as safe actions
Memory divided into short/long-term, personal data protected
Policies and escalations explicitly described
Observability and explainability included in design
Why Implementing Agents Isn't "Just Another Module"
Agentic systems are a new class of software. Their implementation requires a different approach than classical applications. If you simply replace one module with an "agent," you'll only get step automation — without the key power of agency: goal-oriented planning, adaptation, and managed autonomy.
Often, you'll need to rebuild processes: formulate goals and success criteria, set permission boundaries, design escalations, log decisions, and measure results.
Historical Analogy: Factory Electrification
During factory electrification, efficiency wasn't born where "an electric motor was placed on the central shaft," but where processes were restructured: they abandoned a single steam drive and distributed many small electric motors to workstations.
Distributed electric drive changed layout, flows, and safety standards and gave multiple productivity growth. With agents — the same principle: the effect comes when the system around the new class of software is restructured, not when you "screw in a motor" to the old scheme.
Conclusion
An agent is not a "smart microservice," but a goal-oriented execution unit with planning, memory, tools, and policies. It's useful where the environment is variable and the task requires adaptation and autonomy.
To avoid getting "magical chaos," design the agent as a system: goals, security, observability, economics. And check everything against metrics — where the numbers align, agency stops being a fad and becomes a working tool.
This article was originally published in Russian on The Tech.