LLMs reason well, but without a runtime that handles lifecycle, state, and governance, AI agents are unreliable in production.
That’s the pattern I kept running into while working with LLM-based agents.
Modern models like Google Gemini can reason, plan, and invoke tools impressively well. Interactive CLIs and agent frameworks make it easy to prototype workflows in minutes.
But once you try to use these agents for real operational work, cracks appear quickly.
This post explains:
- Why agent systems break down in production
- Why prompts and agent loops are not enough
- What kind of infrastructure is actually missing
The problem: agents are good at thinking, bad at executing
Most agent systems today follow a familiar loop:
- Generate a plan
- Execute a step
- Observe the result
- Repeat
This works surprisingly well for demos.
It fails when:
- A task spans multiple steps
- A process takes minutes or hours
- A failure occurs halfway through
- An action requires approval
- You need to know what actually happened
In practice, agents lack:
- Durable task state
- An explicit execution lifecycle
- Governance and safety controls
- Recovery and resume guarantees
- Auditable behavior
When something goes wrong, the system usually does one of two things:
- Restart everything from scratch
- Fail silently
Neither is acceptable in production.
Why interactive CLIs and agent frameworks don’t solve this
Interactive tools and agent frameworks are not flawed — they’re just scoped differently.
They are optimized for:
- Human-in-the-loop usage
- One-off execution
- Exploration and iteration
- Fast feedback
They are not designed to be:
- Long-running execution engines
- Durable workflow systems
- Policy-enforced runtimes
- Auditable automation layers
This distinction matters.
An interactive agent loop is not the same thing as an execution runtime — just like a shell script is not the same thing as a workflow engine.
The missing layer: why AI agents need an execution runtime
What’s missing between LLM reasoning and real-world automation is a runtime layer that treats AI work like actual work.
That means introducing first-class concepts such as:
- Task lifecycle (created → running → paused → completed / failed)
- Persistent state and checkpoints
- Explicit retries and failure handling
- Approval and policy enforcement
- Observability and traceability
Without this layer, agents remain:
- Impressive
- Unreliable
- Unsafe to trust with real operations
A concrete example
Imagine an AI Ops Analyst tasked with generating a weekly incident report:
- Read incident data
- Analyze trends
- Generate a report
- Request approval
- Send the report
If step 3 fails:
- Should the system restart everything?
- Retry only that step?
- Pause and ask for human input?
- Resume later from the last checkpoint?
Most agent systems today don’t know how to answer these questions.
A runtime does.
What an execution runtime actually does
An execution runtime is deliberately boring — and that’s a good thing.
It focuses on:
- Lifecycle management instead of prompting tricks
- State persistence instead of stateless loops
- Governance instead of trust
- Recovery instead of hope
The LLM still plans and reasons.
The runtime decides how and when actions happen.
This separation turns an assistant into something closer to a governed coworker.
A reference implementation: Taskcraft Runtime
While exploring these problems, I built Taskcraft Runtime — an open-source, Gemini-first execution runtime designed to explore this missing layer.
Taskcraft is intentionally not:
- A chatbot
- A UI
- A prompt framework
- A SaaS product
It is a runtime.
It provides:
- Structured task lifecycles
- Persistent state and resume
- Policy enforcement and approval gates
- Explicit execution boundaries
- Observability by default
The current implementation runs on Gemini, but the architecture is deliberately model-agnostic.
The goal is not to replace existing agent tools — but to complement them with execution guarantees they intentionally don’t provide.
Why this matters now
As LLMs get more capable, the bottleneck is no longer reasoning.
It’s reliability.
The difference between:
“AI that can do things”
and
“AI you can trust with work”
is infrastructure — not prompts.
Execution runtimes are how we cross that gap.
Closing thoughts
Agent demos will keep getting better.
But production systems are built on:
- Clear boundaries
- Predictable behavior
- Explicit failure handling
- Governance and auditability
If we want AI coworkers — not just assistants — execution must be treated as a first-class problem.
Links
Taskcraft Runtime (v0.1.0)
https://github.com/BonifaceAlexander/taskcraft-runtime
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