As AI coding agents become increasingly capable of generating software at machine speed, a new engineering challenge is emerging beneath the surface: whether these systems truly understand the environments they operate in. While many AI development tools focus on accelerating code creation, concerns are growing over reliability, deployment stability, and the hidden risks of autonomous software modification in production systems.
In an interview with Korea IT Times, Hasibul Haque, CEO of San Francisco-based Causal Dynamics Lab, explained why current AI coding agents still fail to properly understand the operational structure of real-world infrastructure. He also discussed why enterprises need to redesign software verification frameworks as AI-generated code increasingly expands across cloud, policy, and operational environments. Haque further shared his views on how the Production World Model (PWM), the rise of “Verification Debt,” and causal simulation-based development environments could reshape the future structure of software engineering.
What follows is a Q&A with Hasibul Haque
Your research argues that current AI coding agents lack visibility into production systems. How do you define this “blindness” in practical engineering terms?
In practical terms, this blindness is a "context window and reasoning problem". Current agents treat a codebase as flat text, utilizing statistical patterns to predict the next token rather than understanding the system's "physics". They lack the "imaging" required to see how a code change interacts with environmental topology, security policies, and runtime dynamics. This results in code that passes unit tests but causes catastrophic failures upon deployment because the agent cannot see the hidden dependencies of the live environment.
Cielara Code focuses on code localization as a key bottleneck. Why is identifying where to make a change more critical than generating the change itself?
Our instrumentation of thousands of agent sessions reveals that over 80% of agent activity is wasted on navigation, file reads (56.8%) and grep (24.2%), while actual edits account for less than 1%. When localization fails, the output is functionally inaccurate. For instance, as documented in GitHub issue #42796, a 70% drop in pre-modification research behavior led to "spliced comments," where agents incorrectly inserted declarations between documentation blocks and functions, breaking semantic association.
We also observed the reappearance of banned abbreviated variable names (e.g., buf, len) and the inclusion of prohibited temporal references like "Phase 2". Generating code is now a commodity; the true engineering challenge is accurately localizing the fault within a multi-dimensional system before updating the code itself.
You introduce a six-layer causal ontology and a Production World Model. How does this differ from existing approaches such as static analysis or observability tools?
Traditional observability is a "better ambulance", it tells you where the crash happened after the fact. Static analysis is limited to the syntax of the code itself. Our Production World Model (PWM) is a deterministic simulation layer that connects six dimensions: functional intent, historical rationale, ownership provenance, policy constraints, environmental topology, and runtime dynamics. This provides a "glass box" understanding that allows us to trace a failure back to a specific commit and original developer intent, something static tools cannot replicate.
The concept of “Verification Debt” is becoming more prominent. How should enterprises rethink their development workflows as AI-generated code increases?
Enterprises must pivot toward "Hyper-Accelerated Defense". The 2025 DORA report indicates that AI coding tools have already led to a 7.2% drop in deployment stability. Manual review is no longer a viable strategy when agents produce code faster than teams can validate it. Workflows must shift to deterministic simulation, where every proposed change is resolved against a causal model of the production environment before it ships to prevent what AWS CTO Werner Vogels calls "dynamic verification debt".
Cielara is positioned as a verification layer rather than a replacement for coding agents. How do you see this layer evolving as AI agents become more autonomous?
As agents become more autonomous, the verification layer must evolve into a "permanent reasoning layer". Instead of just providing guardrails, Cielara will function as an authoritative source of system "physics" that any agent can query before making a decision. We move from a world of reactive logging to one of "strong, directive policy," where the verification engine understands the downstream effects of an autonomous agent's choices before they are executed.
Looking ahead, you mention expanding into full causal simulation. What would a fully simulation-driven development environment look like in practice?
It would be an environment that delivers deterministic simulation of code changes across the entire stack: code, infrastructure, policy, and operations. In practice, this means a developer or agent can explore a possibility space and see the precise blast radius of a change on cloud reliability and revenue before a single bit is modified in production. We are essentially building a "Large Physics foundation Model" (LPM) for software systems.
Many enterprises are concerned about reliability and risk. What measurable impact have early users seen in production environments?
Our current users, including 11 Fortune 100 companies, have realized a 30% to 40% reduction in compute costs per task by eliminating failed runs and wrong-file edits. In independent benchmarks like MULocBench, Cielara Code achieved a 0.774 localization accuracy, outperforming Claude Code and OpenAI Codex. For engineering teams, this translates into a 20% reduction in mean task time and significantly higher deployment stability.
If you are interested in collaborating with Korean businesses, which types of companies or sectors do you believe would be mutually beneficial partners?
We see immense potential in collaborating with sectors where infrastructure reliability is non-negotiable. This includes South Korea's world-leading semiconductor manufacturing, financial services, and telecommunications sectors. Companies operating at the scale of Samsung or LG face the most acute "verification debt" as they integrate autonomous agents into their legacy and cloud environments. Our causal intelligence platform is designed specifically for these high-scale, mission-critical operations.
