AI agents combine two abilities: a language model's reasoning and the power to take actions in your tools. Point one at a task and it figures out a plan, then executes it, deciding what to do and then doing it. That pairing of judgment and action is what makes agents feel magical.
The catch is that most agents run both halves through the model on every execution: the LLM reasons and the LLM acts. That's where slowness, cost, and hallucinations creep in, because a model carrying out each action can drift or invent steps. The 2026 breakthrough keeps the best of both worlds and splits the jobs: let AI reason to craft the right workflow once, then run the actions as deterministic automation. You get an agent's judgment at setup and hallucination-free execution in production. That's how Caddi is built.
The basics
What is Reasoning-only agents?
AI agents are systems that pair a language model's reasoning with the ability to act, calling tools, moving data, and completing multi-step tasks. They decide what to do and then do it, rather than waiting on a prompt for each step.
What is Caddi?
The deterministic AI automation platform for ops and admin teams. Ops teams teach Caddi their workflows over a screen share, and then Caddi runs them reliably hundreds of times a week.
What's changing in 2026
The first agent wave ran everything through the model: the LLM both reasoned about the task and carried out each action, every run. That's the right tool for novel work, but when a model performs the actions it can drift, mis-step, or hallucinate, and you pay full inference cost and sequential latency on every execution.
In 2026 teams stop forcing one tool to do both jobs. The LLM does what it's best at, reasoning, to understand and craft the workflow at setup. The actions then run as deterministic automation: parallel, cheap, and identical every time. You keep the agent's intelligence where it adds value and gain execution you can actually trust.
AI agents 1.0 → 2.0
The next version keeps both halves of an agent, reasoning and action, but lets each run where it's strongest.
Reasoning to craft the right workflow
The hard part of any process is figuring out the right sequence of steps and decisions, exactly what a language model is good at. The 2026 model uses AI reasoning to understand the task and craft the workflow once, so you get an agent's judgment about what should happen, without making the model the thing that happens on every run.
Automation to execute the actions, hallucination-free
Once the workflow is crafted, the actions don't need a model, they need to run exactly as specified. The 2026 model executes them as deterministic automation, so every API call, document read, and system update happens precisely as designed. No drift, no mis-clicks, no hallucinations in production.
Decided once, validated, then fixed
Because reasoning happens at setup, you can validate the workflow once and know it's correct, then run the identical deterministic code every time. You verify it once instead of hoping the agent behaves on each execution, the confidence of code with the intelligence of an LLM behind the design.
Faster and cheaper at scale
Running both reasoning and actions through a model on every execution is slow and expensive. When the crafted workflow runs as deterministic code, steps execute in parallel for a fraction of the time and pennies of the cost, you're not re-paying a model to re-think and re-do a solved task hundreds of times a week.
The old way vs. the 2026 way, at a glance
| Reasoning-only agents | Caddi | |
|---|---|---|
| What the LLM does | Reasons and acts every run | Reasons once to craft the workflow |
| How actions run | LLM executes (can hallucinate) | Deterministic automation, exact |
| Reliability | Can drift run to run | Validated once, then fixed |
| Execution | Sequential reasoning each run | Parallel, deterministic code |
| Cost per run | Full token cost every time | Cheap to run once compiled |
| Auditability | Opaque reasoning | Auditable API calls & logs |
How they score where it counts
Which fits your situation?
Both models have a place. Tap the scenario closest to yours to see which approach wins — and why.
Which fits your situation?
Reasoning-only agents
Open-ended, creative, one-time work is exactly what a reasoning-only agent is built for, thinking fresh each time is the point.
Frequently asked questions
What are AI agents?
AI agents are systems that pair a language model's reasoning with the ability to take actions, calling tools, moving data, and completing multi-step tasks. They decide what to do and then do it, which makes them powerful for open-ended work but harder to trust when a model performs every action on repeatable processes.
How are AI agents changing in 2026?
The winning pattern combines the best of both worlds: use LLM reasoning to craft the right workflow once, then run the actions as deterministic automation. That keeps an agent's judgment at setup while making execution faster (parallel), cheaper (no tokens per run), and hallucination-free in production.
How do you stop AI agents from hallucinating in production?
Separate reasoning from action. Let the LLM reason to design and validate the workflow at setup, then execute the actions as deterministic code so every step runs exactly as specified. You verify it once instead of hoping the model behaves on each run, which is how platforms like Caddi keep production predictable and auditable.
Do I still need AI agents if I use deterministic automation?
They're complementary, and the best approach uses both. Use reasoning-only agents for novel, exploratory, or creative tasks where thinking fresh each time adds value. For repeatable workflows, use AI reasoning to craft the workflow once, then deterministic automation to execute it, the best of both worlds.
The best of both worlds: reasoning and reliable action
See Caddi build a workflow from a screen recording and run it across 70+ tools. Explore real examples, compare Caddi to the tools you know on the comparison hub, or book a demo.
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See Caddi in action
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