From Prototype to Profit: How IBM's CUGA Redefines Enterprise Agents
IBM’s CUGA pilot shows that enterprise agent value depends less on leaderboard glory than on governed tool use, provenance, regression testing, and measurable workflow compression.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Recursive Minds: How ReCAP Turns LLMs into Self-Correcting Planners
ReCAP shows that long-horizon AI agents need not just more context, but better context organisation: recursive planning, parent-plan reinjection, and bounded memory.
The Esperanto of AI Agents: How the Agent Data Protocol Unifies a Fragmented Ecosystem
ADP reframes agent training as a data interoperability problem, showing how typed trajectories can turn scattered agent datasets into reusable fine-tuning infrastructure.
The Missing Metric: Measuring Agentic Potential Before It’s Too Late
APTBench shows why general LLM benchmarks are weak signals for agent readiness, and how trajectory-derived tests can diagnose agentic potential before expensive post-training begins.
When Agents Learn to Test Themselves: TDFlow and the Future of Software Engineering
TDFlow shows that coding agents become far more useful when humans define correctness as executable tests and agents are constrained to solve them.
When Rules Go Live: Policy Cards and the New Language of AI Governance
Policy Cards turn AI governance from scattered compliance prose into a machine-readable runtime contract for autonomous agents.
Agents That Build Agents: The ALITA-G Revolution
Alita-G shows how agents can turn successful task executions into reusable tools, suggesting a practical route from one-off automation to accumulated operational capability.
Agents, Automata, and the Memory of Thought
A mechanism-first reading of how memory constraints turn agentic AI from mystical autonomy into verifiable controller design.
Evolving Minds: How LLMs Teach Themselves Through Adversarial Cooperation
A mechanism-first reading of Multi-Agent Evolve, a self-training framework where one LLM learns by proposing, solving, and judging its own tasks.
Fast but Flawed: What Happens When AI Agents Try to Work Like Humans
A workflow-level reading of human and AI work shows why agents are cheap, quick, programmatic, and still risky in the places businesses most want to automate.