Documentation Index
Fetch the complete documentation index at: https://docs.phrony.com/llms.txt
Use this file to discover all available pages before exploring further.
Workflows vs. agents
Not every problem requires an agent. In many cases, what is described as an “agent” is in fact automation — a system where both the objective and the execution steps are known in advance. A workflow is a program with a known path. It encodes decisions ahead of time and produces a predictable outcome. The inclusion of an LLM does not change this: if both the goal and the method are already specified, the system is not acting autonomously; it is following instructions. An agent is a system that determines its next action during execution, operating under uncertainty in pursuit of a goal. Instead of prescribing a fixed path, the system is given an environment, a set of capabilities, and an objective. The sequence of actions emerges at runtime. Agents operate in a continuous loop of perception → reasoning → action → adaptation.When Phrony fits
Phrony is not intended for every problem. If a system can be expressed as a fixed sequence of steps, it should be. Deterministic workflows are easier to reason about, cheaper to operate, and significantly more predictable. Phrony becomes relevant when the path to the outcome is not known in advance and cannot be reasonably encoded upfront. This typically occurs in four scenarios:1. Uncertainty in decision-making
The number of possible paths grows combinatorially, making upfront encoding impractical. Not all relevant possibilities can be known in advance. Each new conditional branch adds complexity and maintenance cost until the system becomes a dense graph of edge cases.2. Dynamic information discovery
Workflows assume all required inputs are available at the start of execution. In many real-world problems this is not true. The system must decide what information is missing and how to obtain it — by querying external systems, asking follow-up questions, or exploring alternative sources — before it can proceed.3. Non-trivial tool interaction
In workflows, tool usage is predefined: which tool to call, in what order, and under which conditions. This breaks down when tool selection depends on context that evolves during execution. The system must decide not only how to use tools, but which tools to use and why.4. Iterative reasoning
Some problems require multiple passes to reach a conclusion. The system must form hypotheses, test them, revise assumptions, and converge over time. Intermediate results influence future decisions, creating a feedback loop that cannot be modeled as a static flow.Tradeoffs
Adopting Phrony means accepting a different execution model. The benefits of flexibility and adaptability come with clear trade-offs.Predictability
Workflows are deterministic by design. Given the same inputs, they tend to produce the same outputs. Agentic systems powered by LLMs do not offer the same guarantees. Behavior can vary across runs due to differences in context, intermediate results, or model reasoning. Phrony constrains but does not eliminate this variability. By governing how decisions are executed, how tools are accessed, and how state evolves, it reduces the surface area of unpredictability and makes system behavior more consistent.Debugging
In a workflow, failures trace to a specific step in a predefined path. In an agentic system, the path itself is constructed during execution. Phrony addresses this by making execution observable: capturing the reasoning flow, recording all tool interactions, and enabling inspection of how and why decisions were made. While debugging remains more complex than in deterministic systems, it becomes tractable.Reproducibility
Agentic systems may not produce identical outcomes across runs, particularly when external data or probabilistic reasoning is involved. Phrony mitigates this through structured logging, traceability, and replay mechanisms — allowing executions to be analyzed and, to a reasonable extent, reconstructed.Operational complexity
Delegating decisions to runtime introduces inherent complexity. Without proper controls, agentic systems can behave unpredictably, access unintended resources, or produce unsafe outcomes. Phrony’s guardrails — operation allowlists, approval requirements, anomaly detection, and execution limits — exist to close this gap.Design shift
Adopting Phrony requires a shift in how systems are designed:| Traditional workflow | Phrony (agentic) |
|---|---|
| Encode decisions upfront | Define constraints and boundaries |
| Fixed execution path | Execution path emerges at runtime |
| Deterministic output | Governed, observable output |
| Easy to debug (predefined steps) | Requires execution tracing |
| Predictable | Flexible |
The guiding principle: Phrony brings agentic systems closer to the reliability of traditional workflows, without removing their flexibility.