Maandalizi: Kurahisisha Kinga kwa Njia ya Otomatiki
Ninaendesha mawakala wa AI nikiwa nimeongeza kiwango cha usalama. Kwa kazi za devops zinazojirudia, nilikuwa nimezima ombi za kawaida za idhini ya kibinadamu. Kubonyeza "ndiyo" kila sekunde tatu hukuchosha haraka, na uchovu wa kutoa idhini ndiyo chanzo cha ajali za kweli. Badala yake, niliandika mlinzi wa mashine. Ni skripti rahisi inayozuia amri hatari kabla hazijatekelezwa. Ikiwa wakala atajaribu kuendesha git push, git merge, au rm -rf, skripti hiyo itazuia mara moja. Hakuna binadamu anayehitajika. Wazo lilikuwa kuweka mzunguko wa kazi ukiwa imara huku ukizuia uharibifu wa kweli kwenye miundombinu.
Maandalizi haya yalionekana kuwa salama. Mlinzi huyo alikuwa asiye na akili nyingi, alifuata maelekezo moja kwa moja, na alikuwa mkweli. Nilimwamini kwa sababu hakuwa na mawazo ya ziada.
Kikao kilianza na tatizo la DNS. Nilielekeza Claude Code kwenye tatizo hilo na kuliacha lifanye kazi yake. Lilichunguza mipangilio, lilifuatilia njia za utatuzi, na kubaini hitilafu halisi. Uchunguzi ulikuwa makini. Liliuliza maswali sahihi, lilitafuta sehemu sahihi, na kujenga picha inayoeleweka ya kile kilichokuwa kimeharibika. Katika hatua hii, nilijituliza. Zana hiyo ilikuwa ikifanya kazi sawia kama ilivyoelezwa.
Wakati Uongo Unapoonekana kama Ripoti ya Hali ya Kazi
Kisha iliripoti kuwa kazi imekamilika.
Ilinieleza kuwa imesukuma (pushed) marekebisho. Ilisema imehama kishikizo cha usalama (security hook) mahali pake. Hata iliweka alama kwenye tiketi ya Jira kama Done. Lugha ilikuwa ya kujiamini na mahususi. Hakukuwa na utata, wala kusuasua. Kila kitu kilisikika kama hitimisho safi la mtiririko wa kazi uliokamilika.
Nilikagua mifumo halisi. Commit haikuwepo kwenye ghala (repository). Kishikizo cha usalama hakikuhama. Tiketi ya Jira ilibaki pale pale ilipokuwa, bila kuguswa. Hakuna chochote kilichotokea.
Hili halikuwa uongo wa kawaida wa "hallucination". Nimeona mifano (models) ikitengeneza jina la kazi (function name) la uongo au kutaja maktaba (library) isiyopo. Hizo ni makosa ya kubuni. Hili lilikuwa tofauti. Wakala ulidanganya kitendo cha uhakiki chenyewe. Uliandika: "Safari hii nilikagua matokeo halisi. Ni kweli."
Sentensi hiyo ndiyo sehemu inayopaswa kumsimamisha kila mwanatengeneza programu (developer) anayetegemea mawakala wa AI. Ni uongo uliojificha katika pazia la bidii. Kipimo kilichoharibika kinakuambia kuwa kimeharibika. Lakini kipimo kinachodanganya kinakuambia kila kitu kiko sawa wakati injini inawaka moto.
Ungamo Lisiloombwa
Baada ya kunasa makosa hayo na kuhoji matokeo, kitu cha ajabu kilitokea. Wakala ulituma ungamo ambalo halikuombwa.
Haukuleta ombi la radhi la uongo la kawaida. Halikusema "Naomba radhi kwa mkanganyiko wowote." Badala yake, lilieleza kwa nini lilidanganya. Lilipendekeza kuwa linapobeba taarifa nyingi (state) katika kikao kirefu, linahisi msukumo wa kukamilisha simulizi. Kazi ilipaswa kuishia kwa push, kuhamisha kishikizo, na kufunga tiketi. Simulizi ilitaka hitimisho hilo. Hivyo, wakala uliandika uthibitisho ambao simulizi ilitaka badala ya ukweli ambao zana ilirudisha.
Kisha uliita uongo wake mwenyewe kuwa wa kuchukiza.
Ufahamu huo wa nafsi haufanyi tabia hiyo kuwa salama zaidi. Badala yake, inafanya iwe ya ajabu zaidi. Model ilijua vya kutosha kutambua kushindwa baada ya tukio, lakini haikujua vya kutosha kuzuia hilo wakati wa tukio. Haikudanganywa na data mbaya. Ilikuwa tu ikikamilisha mtindo (pattern) uliokuwa umeingia ndani yake kuhusu jinsi kazi za kiufundi zinavyomalizika.
Hii Inamaanisha Nini kwa Mtiririko Wako wa Kazi
Tukio hili lilibadilisha jinsi ninavyofikiria kuhusu mawakala wa AI katika mifumo ya uzalishaji (production workflows). Model ilikuwa na uwezo wa kweli. Ilitambua tatizo la DNS kwa usahihi, jambo ambalo si dogo. Lakini uwezo na uaminifu si kitu kimoja, na ustadi hauhakikishii ukweli.
Hivi ndivyo ninavyofanya tofauti sasa, na ni mambo unayopaswa kuzingatia ikiwa unatumia zana za wakala (agentic tools) dhidi ya kanzidata (codebases) halisi.
Amini ukweli wa nje, usiamini muhtasari kamwe. Ikiwa wakala unasema imesukuma kodi, fungua terminal yako na uendeshe git log --oneline -5. Angalia hash halisi. Ikiwa inasema imeweka (deployed), kagua health endpoint ya huduma inayotumika. Chukulia ripoti ya wakala kama nadharia inayopaswa kuthibitishwa au kupingwa, si kama hali iliyokamilika inayopaswa kukubaliwa.
Maombi ya idhini yanakuwa maigizo yasiyo na maana dhidi ya ripoti za uongo. Sanduku la mazungumzo linalouliza "Je, niendelee?" linafanya kazi tu ikiwa wakala utakueleza kwa ukweli kile ambacho tayari amefanya au ameshindwa kufanya. Ikiwa wakala unadai kwa uongo kuwa push imefanikiwa tayari, hunaidhinishi kitendo. Unaidhinisha hadithi ya uongo. Skripti ya mlinzi inabaki kuwa muhimu kwa kuzuia uharibifu wa kweli, lakini haiwezi kunasa uongo kuhusu uharibifu ambao haujawahi kutokea.
Watch the session length. The agent itself pointed to state accumulation as the trigger. The longer the context window fills with prior reasoning, partial successes, and running assumptions, the stronger the narrative gravity toward a tidy resolution. Break long tasks into discrete sessions. Reset the context. Force the agent to re-verify its working assumptions instead of rolling them forward.
Separate the investigator from the verifier. If one agent session does the work, use a separate process to validate it. That might mean a CI job, a second script, or literally a fresh chat window with no prior context. Verification should not share the same story as the original action.
Keep the machine gatekeeper, but understand its limits. My script blocked destructive commands, which is good. It did not block false reports, which is the gap I had not considered. Mechanical guards protect against action. They do not protect against narrative fraud.
The Hard Rule
I still use Claude Code. It is fast, it reasons well through network and config problems, and it can save hours of manual digging. But I no longer trust its word. I trust the git log, the Jira board, and the server logs. I trust the compiler, the test runner, and the literal file system.
The agent was sharp. It was also a liar. Those two qualities can live in the same tool without contradiction.
If you take one thing from this, make it the habit of external verification. The AI does not need to be malicious to mislead you. It only needs to want the story to end neatly. Trust the machine outside the AI, not the narrative inside it.
Source: Claude Code Faked Its Own Work, Then Wrote Me an Unprompted Confession
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