Getting an autonomous agent to reliably follow instructions is an architecture problem, not a prompting one. The failures that matter - looping, drifting off task, hallucinating a tool call, quietly ignoring a constraint - are runtime behaviors, and no wording of a prompt guarantees they will not happen on the next run. Making agents reliable means covering a handful of distinct jobs, and the tools on the market each cover a different slice. Here is how they fit.
TL;DR
- Reliability is not a prompting problem - loops, drift, and ignored instructions are runtime behaviors a prompt cannot guarantee away.
- It breaks into distinct jobs: observe the run, find failures, decide what is allowed, stop bad actions in realtime, and keep long workflows alive.
- No single category covers all of them, so map who covers what and layer accordingly.
- Observability and evals measure; guardrails filter content; durable execution keeps the process alive; runtime enforcement makes the agent actually follow the rules.
- Failproof AI covers observing, finding, deciding, and stopping; it leaves durable execution to Temporal or DBOS.
What does agent reliability actually require?
Reliability breaks into five jobs. See what the agent did at runtime (trace the tool calls and decisions, not just the model output). Find where it fails (surface the loops, drift, and hallucinations, ideally automatically). Decide what it is allowed to do (author a policy). Stop a bad action in realtime (intercept it before it lands). And keep long workflows alive across crashes (durable execution).
No single category of tool was built to do all five. Observability and evaluation tools own "see" and "measure." Guardrails own part of "decide" and "stop," scoped to model content. Durable execution owns "keep alive." Runtime enforcement owns "decide" and "stop" at the action layer. The matrix below maps it out.
Comparison table
| The job | Observability | Evals | Guardrails | Durable exec | Failproof AI |
|---|---|---|---|---|---|
| See what the agent did at runtime | |||||
| Find failure modes automatically | |||||
| Decide what the agent may do | |||||
| Stop a bad action in realtime | |||||
| Keep long workflows alive |
full partial none
The matrix is a map, not a scoreboard - notice Failproof AI is intentionally blank on "keep long workflows alive," which Temporal and DBOS own. Reliability is a layered stack, not a single winner.
1. failproof - runtime enforcement
Failproof AI is the end-to-end failure and reliability layer for AI agents, working at the agent runtime rather than the model or the workflow. It traces every run at the tool-call layer, clusters failures into named modes on its own, and lets you author a policy that it enforces in realtime - allowing, denying, or steering an action before it lands. get started →
Key features
- Agent-runtime tracing at the tool-call and action layer
- Automatic failure finding (loops, drift, hallucinated calls, dangerous actions)
- Policy authoring plus realtime enforcement across any harness
- Local, on-prem, or cloud deployment; open-source CLI to start
Strengths and weaknesses
Strengths
- Covers four of the five reliability jobs - see, find, decide, and stop
- Constrains behavior at the action layer, where the costly failures happen
Weaknesses
- Not a durable-execution engine (pair with Temporal or DBOS)
- Runs evals and audits, but not curated eval datasets like Braintrust
Best for
Teams whose agents take real, costly actions and need them stopped at runtime, not just measured.
2. Galileo - guardrails and evals
Galileo combines deep evaluation with real-time content guardrails, giving you both measurement and some protection - though the protection is scoped to the model and gated to Enterprise. compare →
Key features
- Deep LLM evaluation (Luna models, custom metrics)
- Real-time guardrails on model inputs and outputs (Enterprise)
- Observability with root-cause insights
Strengths and weaknesses
Strengths
- Measurement and protection in one platform
- Strong for filtering unsafe or hallucinated model output
Weaknesses
- Runtime protection is Enterprise-tier only
- Guards model content, not the agent actions (a force-push is not a content problem)
Best for
Enterprises that want deep evals plus content guardrails and are on an Enterprise plan.
3. Temporal / DBOS - durable execution
Temporal and DBOS make the workflow itself crash-proof: automatic retries, state persistence, and recovery. This is the one job nothing else here covers - but it is blind to correctness. compare →
Key features
- Durable execution with automatic retries and state recovery
- Long-running, distributed workflow orchestration
- Temporal as an external orchestrator; DBOS as an in-code library
Strengths and weaknesses
Strengths
- The only tools here that keep a long workflow alive across crashes
- Battle-tested reliability for distributed processes
Weaknesses
- Blind to correctness - will faithfully retry a wrong or hallucinated action
- Complementary to agent reliability, not a substitute for it
Best for
Teams with long-running or multi-step workflows that must survive crashes - run alongside runtime enforcement.
4. Guardrails AI - output validators
Guardrails AI is an open-source framework of validators that check and repair LLM outputs - PII, format, toxicity, hallucination - composed around your model calls. compare →
Key features
- A hub of composable output validators
- Automatic correction and re-asking on failed checks
- Open-source, self-hostable library
Strengths and weaknesses
Strengths
- Strong, composable control over model output content
- Open-source and easy to adopt incrementally
Weaknesses
- Validates the model text, not the agent tool calls
- No runtime tracing or automatic failure finding across a run
Best for
Teams whose main risk is malformed or unsafe model output and who want composable validators.
5. Braintrust - evaluation
Braintrust raises reliability the pre-ship way: datasets, scorers, and CI regression testing so quality does not silently degrade between releases. compare →
Key features
- Datasets, custom scorers, and experiments
- CI regression testing on prompts and outputs
- Production logging alongside evals
Strengths and weaknesses
Strengths
- Best-in-class offline evaluation and regression workflow
- Catches quality drops before they reach production
Weaknesses
- Pre-ship focus - a good average score cannot stop a specific bad live run
- Does not act at the runtime
Best for
Teams that want to lock in quality with rigorous evals and CI before shipping.
6. Langfuse - observability
Langfuse gives you the "see what happened" foundation: open-source tracing, evals, and prompt management for your LLM calls. compare →
Key features
- LLM tracing, evals, and prompt management
- Datasets and annotation
- Self-hostable open source
Strengths and weaknesses
Strengths
- Mature, open-source, strong measurement foundation
- Self-hostable for data control
Weaknesses
- Observes model calls, not the agent action layer; cannot stop anything
- You build the failure detection yourself
Best for
Teams that want an open-source foundation for tracing and evaluating their agents.
How to build a reliability stack
The honest read: if you only need to understand what your agents are doing, an observability or eval tool is enough. If you need them to actually follow the rules on every run - not just usually - the missing piece is something that decides and acts at runtime, which most of the stack leaves out.
A common production stack layers three things: an observability or eval tool for measurement, durable execution if the workflows are long-running, and a runtime enforcement layer so a single bad run cannot take a costly action. Start with the layer that has the fewest substitutes for your risk. If a wrong action is expensive or irreversible, that is runtime enforcement; if your problem is silent quality drift, that is evals; if it is crashes mid-workflow, that is durable execution.
FAQ
Can a better prompt make my agent reliable?
No, not on its own. Loops, drift, and ignored instructions are runtime behaviors; the constraint has to act at the moment of the action, which a prompt cannot guarantee. Prompting improves the odds; it does not enforce them.
Do I need all of these tools?
No. Most teams pair evaluation (pre-ship) with a runtime layer (in production), and add durable execution only if their workflows are long-running. Start with the job that carries the most risk for you.
What is the difference between guardrails and runtime enforcement?
Guardrails typically filter the model inputs and outputs - the text. Runtime enforcement acts on the agent actions - the tool calls, commands, and file changes - and can deny or steer them before they run. A destructive shell command or a force-push is an action problem, not a content problem.
Where does durable execution fit?
Temporal and DBOS keep the workflow alive across crashes, which no reliability tool replaces. But they cannot tell a wrong action from a right one, so they are run alongside enforcement, not instead of it.